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writeDate(1033);
DynaFile is the industry-leading electronic employee filing system tailored for HR.
GREENWOOD VILLAGE, Colo. (PRWEB) November 01, 2022
DynaFile, the industry-leading electronic employee filing system, and TopBloc, a leading Workday Services Partner, have partnered to develop a new Workday integration that enables customers using both Workday and DynaFile to save meaningful time while managing their HR-related documentation. With this new electronic filing solution, documents in Workday can be automatically filed in your DynaFile cloud, where they can be instantly found, provided to auditors, and easily managed long-term.
The Workday document integration uses your HCM data in DynaFile to complement your workflow. After documents are filed, you’ll have seamless role-based access, retention schedules for older records, the ability to create approval workflows, and much more.
To learn more about enhancing your Workday document management experience, please contact sales@dynafile.com or call 888-510-3453.
About DynaFile
For over 20 years, DynaFile has been the industry-leading electronic filing solution for HR. Developed with simplicity in mind, DynaFile was created in 2000 to help companies manage their HR employee files more efficiently. The scan-to-cloud document management software provides a simple and effective solution to sync with your HCM and HRIS and streamline processes through scanning automation, online file storage, cloud collaboration, automated onboarding integrations, custom reporting, and a compliance toolkit. https://www.dynafile.com
About TopBloc
TopBloc is a leading Workday Services Partner that specializes in helping customers improve and sustain their businesses with Workday. Providing full spectrum Workday services, TopBloc leverages proprietary technology to deliver fast and efficient Workday deployment services and flexible post-production support that optimizes existing Workday solutions. To learn more about how TopBloc makes Workday work for you, contact team@topbloc.com. https://topbloc.com
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December 16, 2022 09:31 ET | Source: FMW Media Works Corp FMW Media Works Corp
New York City, New York, UNITED STATES
NEW YORK, Dec. 16, 2022 (GLOBE NEWSWIRE) — FMW Media’s New To The Street / Newsmax TV announces the broadcasting line-up of its nationally syndicated 1-hour TV show this Sunday, December 18, 2022, airing time 10-11 AM ET.
New to The Street’s 417^th TV show line-up features the following nine (9) corporate interviews:
1). Quantum Computing- Quantum Computing, Inc.’somputing, Inc.’s (NASDAQ: QUBT) ($QUBT) interview with Bill McGann, Chief Technology Officer.
2). Ecology Solutions – VRM BioLogik Group’s /The Sustainable Green Team’s (OTCQX: SGTM) ($SGTM) interviews with VRM Biologic Group’s Founder, Ken Bellamy, and CEO, Kellie Walters.
3). American Made Product – American Rebel, Inc.’s (NASDAQ: AREB) ($AREB) interview with Andy Ross, Chairman, and CEO.
4). Hemp Cigarettes – Hempacco Co., Inc.’s (NASDAQ: HPCO) ($HPCO) interviews with Sandro Piancone, Co-Founder/CEO, and Dr. Justin Titus, Chairman.
5). Cryptocurrency- LGCY Network’s (CRYPTO: LGCY) ($LGCY) / Quantum International Corp.’s (OTCPink: QUAN) ($QUAN) interview with Justin Waiau, Project Manager at LGCY Networks / CEO at Quantum International Corp / LootUP.
6) Specialty Pharmacy – Golden Developing Solutions, Inc.’s (OTCPink: DVLP) ($DVLP) interview with Stavros Triant, Chief Executive Officer.
7). Email/Messenger Privacy Solutions – Sekur Private Data, Ltd.’s (OTCQX: SWISF) (CSE: SKUR) (FRA: GDT0) interview with Alain Ghiai, CEO.
8). Consumer Products – Green Globe International, Inc.’s (OTCPink: GGII) ($GGII) interview with Dr. Stuart Titus, Chairman of The Board.
9). “Sekur Privacy & Sekur Security Segment” interview with internet privacy expert Mr. Alain Ghiai, CEO, Sekur Private Data Ltd. (OTCQX: SWISF) (CSE: SKUR) (FRA: GDT0) (Sekur®).
From the Nasdaq MarketSite studio, Bill McGann, Chief Technology Officer of Quantum Computing, Inc. (NASDAQ: QUBT) ($QUBT) (“QCI”) talks with New to The Street’s Co-Hosts Jane King and Hunter Gaylor. Engineers and scientists use quantum computers to solve some of mankind’s more complex problems. But Noisy Intermediate Scale Quantum (NISQ) computers are significantly limited in their usefulness for solving complex real-world problems. Bill explains what makes Quantum Computing different than competitor products and how the Company’s Entropy Quantum Computer (EQC) is a revolutionary approach to quantum computing. EQC uses the environment’s noise sources, which other quantum computer systems ignore or cannot process, and optimizes that data to solve complex problems with multiple variables. Inventory supply chains, transportation logistics, finance, and medical services are industries with many variables interacting that need a reliable and affordable solution for problems. Bill explains a case study using EQC with a bank looking to improve monetary fraud detection and how the Company successfully helped the bank with new fraud alert tools. Many other industries can quickly deploy a ready-to-run QCI solution. With a low environmental footprint, hardware that runs at room temperature, and processes solutions based on all input variables, Quantum Computing, Inc. can offer time-sensitive solutions without extraordinary cost to deploy. The on-screen QR code is available during the show; download or visit Quantum Computing, Inc. – https://www.quantumcomputinginc.com/.
New to The Street airs TV Host Jane King’s Nasdaq MarketSite studio interview with VRM BioLogik Group’s Founder, Ken Bellamy, and CEO, Kellie Walters. Ken talks about the 2023 COP28 Climate Change Conference and explains to viewers the leadership initiative in Dubai with a desert waste program. Using VRM BioLogiks hydrosynthesis technology and The Sustainable Green Team (OTCQX: SGTM) ($SGTM) mulching methods, the small desert area is growing water and significantly improving soil nutrients and microbes. From the air, one can see the improved soil composition compared to the surrounding desert, capable of growing crops. Kellie talks about the invitation to SGTM’s inaugural global summit in Florida on December 15-16, 2022. As featured guests and keynote speakers, VRM explains the business collaboration and relationship between VRM and SGTM. SGTM has a distribution agreement with VRM to bring products and technology into the North American market. Kellie sees the summit as a great venue to explain the HumiSoil product that greatly improves all soil types and increases worldwide agricultural yields. Ken details how both human and organic waste can be used together and treated accordingly to create usable soils for crop yields. The US retail market for small farms and home gardeners is great for HumiSoil. With the US drought, lawns and gardens can use a product that grows water and maintains and improves the soil. The Company expects a fantastic 2023 operational year and remains steadfast on its mission of restoring topsoil’s water content and nutrition for 25% of the globe’s arable lands, millions of acres worldwide. The on-screen QR code is available during the show; download or visit VRM BioLogik Group – https://www.vrm.science/ and The Sustainable Green Team – https://www.thesustainablegreenteam.com/.
Andy Ross, Chairman/CEO of America Rebel, Inc. (NASDAQ: AREB) ($AREB), is back on New to The Street giving a corporate update. Speaking with TV Host Jane King from the Nasdaq MarketSite studio, Andy talks about the Company’s “American Made” products and why he believes American Rebel is “America’s Patriotic Brand.” With gun safes, weapon lockers, concealed-carry weapons (CCW) apparel and backpacks, and its e-bike, sales continue to grow strongly. The Company has five distribution centers and about 400 retail outlets selling American Rebel products. Andy explains the recent acquisition of the original equipment manufacturer of the Champion and Superior products and how that is accretive to The Company’s operations. For ten years, Andy had an Outdoor Cable channel show about bow hunting; as the show grew, he wrote songs played during those TV airings. His one song, “American Rebel” inspired him to start the Company in 2015. The Company’s goal is to create all sorts of “American Rebel” products to grow their brand name recognition. The Company’s e-bike is just one example of a branded product they sell. The e-bike market is growing, and the high-quality bike and its price-point constitute a good consumer purchase. American Rebel will feature its e-bike and other products at the upcoming SHOT trade show in Las Vegas in January 2023. Viewers can learn more about the Company’s mission and products through its strong social media presence, websites, and websites like www.americanrebel.com and www.andyross.com. The on-screen QR code is available during the show; download or visit America Rebel, Inc. – http://www.americanrebel.com/.
From the Nasdaq MarketSite studio, TV Host Jane King and viewers learn more from Hempacco Co., Inc.’s (NASDAQ: HPCO) ($HPCO) Sandro Piancone, Co-Founder/CEO, and Dr. Stuart Titus, Chairman. The Company’s hemp Disrupting Tobacco™ products are hemp cigarettes, smoked in place of tobacco products. The worldwide tobacco industry is a $1T marketplace, and the Company’s Disrupting Tobacco™ products are working on gaining market share. Sandro talks about HPCO’s joint venture with High Sierra Technologies, Inc. created a JV entity, Organipure, Inc. Using High Sierra Technology’s proprietary smell removal technique, Organipure, Inc. establishes a hemp product, “Smell-Free.” Many don’t like the smell of hemp and cannabis when smoked. During the segment, Jane smelled two hemp containers, one naturally occurring and the other treated with Organipure’s smell removal technology. The odorless-hemp product can now compete in public spaces, where smoking it without the smell like cannabis can ensue. Sandro explains that casinos in Vegas are constantly asking people not to smoke marijuana in their casinos, hemp is not marijuana, but it does smell similar. Dr. Titus explains how CBD can infuse more rapidly for immediate relaxation from smoking hemp. The Company can add terpenoids and other flavors to the hemp and its rolling papers. Marketing the “Smell–Free” hemp cigarettes is an add-on to the marketing of its smokable hemp products. The successful celebrity joint venture partnerships with Cheech & Chong and Rick Ross now can offer the “Smell-Free” cigarettes in addition to the Real Stuff, Hemp Hop, and Hemp Smokable products. Since the passing of The US Farm Bill of 2018, the possibility of using hemp cigarettes as a smoking sensation has been gaining popularity. Hempacco is the first hemp product company that trades on the Nasdaq. With over 64 million Americans using some form of CBD product, the overall market demographics are strong for continued growth at the Company. The on-screen QR code is available during the show; download or visit Hempacco Co., Inc. – https://hempaccoinc.com/ & https://realstuffsmokables.com/.
Mr. Justin Waiau, Project Manager at LGCY Network (CRYPTO: LGCY) ($LGCY) and CEO at Quantum International Corp. (OTCPink: QUAN) ($QUAN) / LootUP is back at the Nasdaq MarketSite studio for an interview with TV Host Jane King. Justin gives an update regarding the $LECY blockchain ecosystem, which enables end-users and developers to participate and create using a dAPP blockchain. The network updated its wallet extensions, integrated the newest API, made an informative developers guide, and is working on new product roll-outs. The Company’s LootUp App, based on the LGCY blockchain, offers a unique and innovative payment platform that never holds end-users funds and is not vulnerable to account shutdowns or freezes. LootUp end users are increasingly enjoying the many benefits of this P2P non-custodial blockchain payment app. A new product app, the CannaLootUp App, based on the LootUp ecosystem, is coming to market soon. The cannabis industry needs a payment system independent of the legacy financial platforms. CannaLootUp App can solve many problems in buying/selling cannabis products because of its non-custodial feature, and the wallet owner of the App have complete control of their assets. The cannabis industry is a strong growing market with many financial needs that can use the unique features built into the CannaLootUp App. Like the LootUp app, the CannaLootUp offers simplified features, which enable end-users to send payments through their approved whitelisted names and confirm/cancel a transaction. It provides invoicing with built-in Smart Contracts for ease and efficiency in purchasing goods/services. Even though 2022 is a tough market for the blockchain and crypto industries, Justin sees a strong 2023 ahead with the growth of LGCY Network’s ecosystem. The on-screen QR code is available during the show to download more info or visit LGCY Network at https://lgcy.network/.
New to The Street welcomes Golden Developing Solutions, Inc.’s (OTCPink: DVLP) ($DVLP) Chief Executive Officer, Stavros Triant. Talking with TV Host Jane King, Stavros gives viewers information about the Company’s history and how they entered the “Specialty Pharmacy” market. The Company migrated from being a cannabis health and wellness company into a health and wellness specialty pharmaceutical entity. With assets and a technology platform owned in the Telemedicine industry, the Company established relationships with physician groups and pharmacy networks. Currently, post-COVID, telemedicine is a crowded industry space, and the Company decided to hold off on a full-roll out until a more favorable time in the future. But the relationships established during the telemedicine platform’s development opened the door to an opportunity in the specialty pharmaceutical market. New drugs with great health restorative benefits are available but not accessible by many or health insurance approved. Local pharmacy outlets sometimes don’t carry these new novel drugs, so a doctor will subscribe an older, less effective drug to a patent, limiting a patient’s access to the latest drug treatments. Stavros says the Company’s online specialty pharmacy can enable patients to get the best drug innovation, bypassing a local pharmacy. The specialty pharmaceutical marketplace is a fragmented industry made up of small businesses in local demographics. DVLP is aggressively seeking acquisitions that can enhance its distribution channels for much-needed drugs, increase its market demographics, looks to reduce operational costs, and improve bottom-line results. Throughout 2023, Stavros tells viewers they intend to build necessary office infrastructures and hire key personnel to handle the expected growth from its operations. The on-screen QR code is available during the show to download more info or visit Golden Developing Solutions, Inc. – https://goldendeveloping.com/.
Mr. Alain Ghiai, CEO at Sekur Private Data, Ltd. (OTCQX: SWISF) (CSE: SKUR) (FRA: GDT0) appears on air with TV Host Jane King to talk about the Company’s encrypted products for secure and private electronic communications. Alain gives viewers a “Year-in-Review” about Sekur’s operational status. 2022 was a great year in reducing marketing costs, increasing product offerings, opening new international markets, offering new business enterprise solutions, developing B2B reseller agreements, and maintaining good cash-on-hand. With over 5000 active subscribers, management expects further increases heading into 2023. The SekurMail / SekurMessenger, SekurVoice, SekurProVideo Conference, and other products are available now or soon. SekurVoice, coming to market in 2023, offers subscribers encrypted voice calls and private voicemail. The Company always focuses on privacy issues and the removal of a possible hack attack on subscribers’ data. Subscribers get cybersecurity protections not available with open-source programs for reasonable pricing. As an added protection to subscribers, Sekur Private Data, Ltd. operates its internet platforms and security businesses under the country of Switzerland’s very tough privacy laws. Alain informs views about an independent financial research report done on the Company. FRC (Fundamental Research Corp.) wrote a paid-for comprehensive financial report regarding the Company’s current and future operations and its price-per-share expectation. Sekur Private Data, Ltd. doesn’t use any Big Tech open-source platforms, never data mines, uses 3^rd party providers, owns, and controls its servers, and never asks for phone numbers. The on-screen QR code is available during the show to download more info or visit Sekur Private Data, Ltd.- https://www.sekurprivatedata.com/ and http://www.Sekur.com.
New to The Street welcomes Dr. Stuart Titus, Chairman of The Board of Green Globe International, Inc. (OTCPK: GGII) ($GGII). Talking with TV Host Jane King, Stuart explains the Company’s business model, a consumer product incubator. The Company and its subsidiaries offer a range of hemp, CBD products, and other consumer goods. The subsidiaries, Hempacco Co., Inc. (NASDAQ: HPCO), Green Star Labs, Inc., HempBox Vending, Inc., and The Real Stuff Smokables, Inc., develop, manufacture, market, and sell fast-moving consumer goods products. The Company owns intellectual properties and consumer goods in the retail, advertising, cannabis, and hemp industries and has a production facility in San Diego, CA. Recently, the Company’s Green Star Lab’s received an NSF/ANSI 455-2 Dietary Supplement GMP Certification. Stuart explains that only about 1% of entities qualify for this certification, which is a considerable accomplishment, giving consumers confidence that all in-house and private-label products have the utmost quality controls. Dietary supplements, hemp, CBD, capsules, oils, gummies, vapes, skincare, and beauty products have the highest possible production standards at GGII and its subsidiaries. The Company Hemp Box Vending subsidiary has over 600 machine kiosks, offering various GGII products, a unique vending operation like no other in the industry. Stuart states that 37 states legalized medical marijuana and 21states legalized recreational uses, and over 64 million Americans use some CBD product; the industry growth is fantastic and expected to continue. Since the passing of the US Farm Bill of 2018, hemp crops and hemp products have been upwardly growing with lots of job creation. GGII will continue to develop business opportunities that create exciting consumables. The on-screen QR code is available during the show; download or visit Green Globe International, Inc. – https://ggiigroup.com/.
On New to The Street TV’s Weekly Sekur Privacy & Sekur Security Segment, internationally acclaimed internet privacy expert Mr. Alain Ghiai, CEO at Sekur Private Data Ltd.  (OTCQX: SWISF) (CSE: SKUR) (FRA: GDT0) talks with TV Host and Multi-media Journalist Ana Berry about the problems with face recognition apps that people use for entertainment, like the Lensa AI app. Ok, it might be fun to age-generate your face recognition into the future and build unique characters featuring kings, queens, and superheroes. Still, it would be best if you asked yourselves how safe the App is in managing your facial recognition data. Alain states that these platforms all share open-source Big Tech platforms known for cybersecurity breaches, and these platforms sell and rent your data profiles. The same problem is ongoing with those DNA services investigating your ancestry; they also share data. Each open-platform account has vulnerability issues, directly or indirectly causing all sorts of data compromises. Big-tech platforms make over $185B a year off data sales, and you get nothing in return, or worse yet, hacked. Alain says anyone using Big-tech open-source email and texting platforms for sensitive and private communications has become a part of what he calls “Data Slavery.” The best possible way to reduce your social media footprint and communicate privately and securely is to become a Sekur product subscriber. Using military encryptions that are invisibilities to hackers for a reasonable monthly fee, a subscriber is confident that electronic communications are secure and private. The Company’s business and servers are in Switzerland, a country with stringent privacy laws, making it illegal to sell data or share communications. Alain is offering 50% off subscription fees; if you subscribe yearly, you automatically get two months free, so the overall savings are about 67%. Use PROMO CODE: Sekur50. Alain looks forward to a beautiful holiday time and looks forward to bringing more updates into 2023. The on-screen QR code is available during the show to download more info or visit Sekur Private Data, Ltd.- https://www.sekurprivatedata.com/ and http://www.Sekur.com. What is your privacy worth this holiday season?
About Quantum Computing, Inc. (NASDAQ: QUBT) ($QUBT)

Quantum Computing,Inc. (QCI) (NASDAQ: QUBT) is a full-stack quantum software and hardware company on a mission to accelerate the value of quantum computing for real-world industry applications, delivering the future of quantum computing today. The combination of QCI’s flagship ready-to-run software product, Qatalyst, with its industry-leading Entropy Quantum Computing (EQC) system, Dirac 1, provides a broadly accessible and affordable enterprise quantum solution capable of solving real business problems now. QCI’s expert team in finance, computing, security, mathematics, and physics has over a century of combined experience with complex technologies, from leading-edge supercomputing to precision sensors and imaging technology, to the security that protects nations – https://www.quantumcomputinginc.com/.
About The Sustainable Green Team, Ltd. (OTC: SGTM) ($SGTM)
The Sustainable Green Team, Ltd. (OTC: SGTM) ($SGTM) is an emerging provider of environmentally beneficial solutions for preserving natural resources and the municipal waste and recycling industries. The Company is a wholesale manufacturer and supplier of wood-based mulch and lumber products, primarily in the Midwest, Southeast, and Ohio Valley regions. The Company also provides arbor care and storm recovery services to municipalities, corporations, and consumers, primarily in the southeastern United States. The Company plans to expand its operations through organic growth and strategic acquisitions that are both accretive to earnings and positioned for rapid growth from the resulting synergistic opportunities identified. The Company’s customers include governmental, residential, and commercial clients – https://www.thesustainablegreenteam.com/.
About VRM BioLogik Group:
Since 1987, VRM BioLogik has developed and implemented technological advances globally that catalyze natural reactions, aiding in soil restoration, resolving environmental emergencies, and cleaning through attaining and sustaining ecosystem balance. VRM’s agricultural product range works to enhance crop yield while restoring the nutrients in soils naturally. Its cleaner product range naturally removes residues from surfaces that harbor viruses and pathogens. The environmental management product range works to address and amend difficult environmental circumstances, including balancing wastewater, control of odor and organics digestion during emergency spills, and eliminating odors. VRM’s livestock product range is a probiotic approach to environmental management in intensive growing situations – https://www.vrm.science/.
About American Rebel Holdings, Inc. (NASDAQ: AREB) ($AREB):
American Rebel, Inc. (NASDAQ: AREB), through its wholly-owned operating subsidiaries, operates primarily as a designer, manufacturer, and marketer of branded safes and personal security and self-defense products. The Company also designs and produces branded apparel and accessories and now intends to enter the E-Bike market – www.americanrebel.com.
About Hempacco Co., Inc. (NASDAQ: HPCO) ($HPCO):
Hempacco Co., Inc.’s (NASDAQ: HPCO) ($HPCO) goal is Disrupting Tobacco’s™ nearly $1 trillion industry with herb and hemp-based alternatives to nicotine cigarettes by manufacturing and marketing herb, spice, and cannabinoid smokables and rolling paper. Hempacco owns The Real Stuff™ functional hemp cigarette and rolling paper brands. Hempacco’s operational segments include manufacturing of smokables and hemp rolling paper, smokable technology development, The Real Stuff™ brand of functional smokables and rolling paper, and Cheech & Chong Hemp Cigarettes and Hemp Hop Smokables with Rick Ross. Learn more at hempaccoinc.com and order products at www.realstuffsmokables.com.
About LGCY Network (CRYPTO: LGCY) ($LGCY)/ Quantum International Corporation (OTCPink: QUAN) / LootUp:
LGCY Network (CRYPTO: LGCY) ($LGCY), founded in August 2020, works on delivering the promise of a truly decentralized blockchain. $LGCY is a DPoS, open-source blockchain protocol with industry-leading transaction speed and flexible utility, and the network aims to be the most decentralized dApp blockchain in the world. One of the shining points of the $LGCY network is the ability of developers to create dApps and customized wallets. The best part is that the applications and uses are limitless because of the capacity for smart contracts to be deployed and executed on its chain. The central focus of the $LGCY Network is the community. A DPoS-based blockchain utilizes a community voting system to determine project funding allocations. $LGCY smart contracts use USDL stablecoin as their gas fee. $ LGCY’s goal is to be the most decentralized blockchain in existence. LootUp, $LOOT, is built on the LGCY Network—the fastest major blockchain with the lowest fees, so users can send and receive funds quickly and easily. Quantum International Corp. is an innovation company. It is in the business of identifying and developing innovative products in the financial world, exploring web 3.0 capabilities – https://lgcy.network, https://lgcyscan.network/, https://lootup.app/, and https://quantuminternationalcorp.com/.
About Golden Developing Solutions, Inc. (OTC: DVLP):
Golden Developing Solutions, Inc. (OTC: DVLP) is a public online health and wellness start-up company that delivers nutritional supplements, including vitamins, tinctures, soft gels, and topical application products. Its pharmaceutical division provides specialty medicine with rapid delivery services and adequate medical support in the United States. The recent four (4) specialty pharmacy acquisitions in 2022 have capacitated the Company’s service offerings to the States of Michigan and Florida with a consolidated revenue of $100M. The Company positioned itself to build shareholder value by setting the highest service, reliability, and safety standards in our rapidly growing industry – https://goldendeveloping.com/.
About Sekur Private Data Ltd. (OTCQX: SWISF) (CSE: SKUR) (FRA: GDT0):
Sekur Private Data, Ltd. (OTCQX: SWISF) (CSE: SKUR) (FRA: GDT0) is a cybersecurity and internet privacy provider of Swiss-hosted solutions for secure communications and secure data management. The Company distributes encrypted emails, secure messengers, secure communication tools, secure cloud-based storage, disaster recovery, and document management products. The Company sells and serves consumers, businesses, and governments worldwide through approved wholesalers, distributors, and telecommunications companies. Contact Sekur Private Data, Ltd. at corporate@globexdatagroup.com or visit  https://www.sekurprivatedata.com and  https://www.sekur.com.
About Hempacco Co., Inc. (NASDAQ: HPCO) ($HPCO):
Hempacco Co., Inc.’s (NASDAQ: HPCO) ($HPCO) goal is Disrupting Tobacco’s™ nearly $1 trillion industry with herb and hemp-based alternatives to nicotine cigarettes by manufacturing and marketing herb, spice, and cannabinoid smokables and rolling paper. Hempacco owns The Real Stuff™ functional hemp cigarette and rolling paper brands. Hempacco’s operational segments include manufacturing of smokables and hemp rolling paper, smokable technology development, The Real Stuff™ brand of functional smokables and rolling paper, and Cheech & Chong Hemp Cigarettes and Hemp Hop Smokables with Rick Ross. Learn more at hempaccoinc.com and order products at www.realstuffsmokables.com.
About Green Globe International, Inc. (OTCPK: GGII):
Green Globe International, Inc. (OTCPK: GGII) is a Fast-Moving consumer goods incubator and holding Company of intellectual property, products, and companies that want to change the world with its products and technology. Green Globe International, Inc. and its subsidiaries develop, manufacture, market, and sell consumer goods, including herb and hemp smokable rolling papers. Its subsidiary, Hempacco Co., Inc. (NASDAQ: HPCO), is disrupting the $1T tobacco industry and has celebrity manufacturing partnerships, private label, and large distributor partnerships. Green Star Labs, Inc. is a partnership with Curated Nutra, LLC, that develops and manufactures CBD, nutritional supplements, and beauty products. The HempBox Vending, Inc. subsidiary owns and manages kiosk vending machines with advertising screens that dispense products while simultaneously advertising products – https://ggiigroup.com/.
About New to The Street:
New to the Street is an FMW Media production that operates one of the longest-running US and International sponsored and syndicated Nielsen Rated programming television brands, “New to The Street,” and its blockchain show, “Exploring The Block.” Since 2009, these brands have run biographical interview segment shows across major U.S. television networks. The paid-for-television programming platforms can potentially reach over 540 million homes in the US and international markets. FMW’s New to The Street / Newsmax televised broadcasting platform airs its syndication on Sundays at 10 -11 AM ET. FMW is also one of the nation’s largest buyers of linear television, long and short-form paid programming – https://www.newsmaxtv.com/Shows/New-to-the-Street & https://www.newtothestreet.com/.
Forward-Looking Statements Disclaimer:
This press release contains forward-looking statements within Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. In some cases, you can identify forward-looking statements by the following words: “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “ongoing,” “plan,” “potential,” “predict,” “project,” “should,” “will,” “would,” or the negative of these terms or other comparable terminology. However, not all forward-looking statements contain these words. Forward-looking statements are not a guarantee of future performance or results and will not necessarily be accurate indications of the times at which such performance or results are achieved. This press release should be considered in all filings of the Companies contained in the Edgar Archives of the Securities and Exchange Commission at www.sec.gov.
CONTACT:
FMW Media Contacts:
Bryan Johnson
+1 (631) 766-7462
Bryan@NewToTheStreet.com 
“New to The Street” Business Development Office.
1-516-696-5900
Support@NewToTheStreet.com 
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/546a4023-7847-4cda-9f81-35d26aba43aa

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April 25, 2022 08:09 ET | Source: Fortune Business Insights Fortune Business Insights
Pune INDIA
Pune, India, April 25, 2022 (GLOBE NEWSWIRE) — The global document management system market size was USD 5.00 billion in 2021 and reached USD 5.55 billion in 2022. The market is anticipated to reach USD 16.42 billion by 2029, exhibiting a CAGR of 16.8% during the forecast period. The rising demand for paperless government and offices due to the extensive adoption of cloud services is expected to propel the market development. Fortune Business Insights™ provides this information in its report titled “Document Management System Market Growth, 2022-2029.”
A document management system is a solution developed to systematically manage documents and files and simplify data management. The rising demand for paperless government and offices may enhance the market growth. Further, the extensive adoption of cloud-based services may enhance the product adoption. These factors may propel the industry’s growth in the coming years.
Key Industry Development


Request a Sample Copy of the Research Report: https://www.fortunebusinessinsights.com/enquiry/request-sample-pdf/document-management-system-market-106615


Report Scope:


Drivers and Restraints
Robust Demand for Workplace Efficiency to Enhance Market Growth
The incorporation of advanced technology such as artificial intelligence, real-time tracking solutions, and cloud computing solutions is expected to surge the product demand. For example, eGrove Systems Corporation announced an integrated advanced agile document and time tracking project management. This factor increased workplace efficiency by using advanced software solutions. Further, incorporating the software enables companies to manage the workplace environment and achieve their goals. These factors may propel the document management system market growth.
However, increasing data privacy concerns and regulatory compliances may hinder market growth.


Click here to get the short-term and long-term impact of COVID-19 on this Document Management System Market.
Please visit: https://www.fortunebusinessinsights.com/document-management-system-market-106615


Regional Insights

Presence of Major Players to Propel Market Progress in North America
North America is expected to dominate the document management system market share due to the presence of several major players. The market in North America stood at USD 2.25 billion in 2021 and is expected to gain a huge portion of the global market share. Further, the presence of a developed digital infrastructure is expected to boost the industry progress.
In Asia Pacific, the rising adoption of DMS solutions by government, manufacturing, and other sectors is expected to boost the document management system adoption. These factors may propel the market growth.
In Europe, rising investments in digital platforms may boost the adoption of the document management system. Further, rising digital platform investments are expected to boost industry progress.






Segments
By component, the market is segmented into solution and services. As per deployment, it is bifurcated into cloud and on-premises. Based on organization size, it is clubbed into large enterprises, and small and medium enterprises. By industry, it is classified into BFSI, IT and telecommunication, government, manufacturing, retail, healthcare, and others. Regionally, it is classified into North America, Europe, Asia Pacific, Middle East & Africa, and South America.
Competitive Landscape
Players Announce Novel Services to Boost Brand Image
The prominent players operating in the market announce novel services to enhance their sales and boost brand image. For example, Google LLC announced an AI-based Lending DocAI service for the mortgage industry. The AI tool helps several mortgage companies in speeding up their document processing. It helps automate routine document reviews by extracting the data required. It is a civilized document that may enable the company to boost its brand image. Further, companies adopt research and development, mergers, acquisitions, and expansions to boost their annual revenues and global market position.


Quick Buy – Document Management System Market:
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Report Coverage
The report provides a detailed analysis of the top segments and the latest trends in the market. It comprehensively discusses the driving and restraining factors and the impact of COVID-19 on the market. Additionally, it examines the regional developments and the strategies undertaken by the market’s key players.
COVID-19 Impact
Rising Dependence Upon Digitization to Foster Market Growth
This Document Management System Market is expected to be negatively affected during the COVID-19 pandemic because of the rising dependence on digitization. The alarming spike in COVID-19 cases leads to restrictions on manufacturing and the closure of activities. Companies focus on developing digital infrastructure to continue their activities and enhance their annual revenues. The accumulation of digital data loads leads to the adoption of effective data management, thereby enhancing the adoption of the product. These factors may propel the market progress during the pandemic.
Companies Profiled in the Document Management System Market Report


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Major Points of Table:
TOC Continued…!
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Jun 17
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While you are creating, editing, or sending digital documents, it is necessary to capture an online signature so that the document can be authenticated. For effectively capturing a legally binding electronic signature online, there is a need for an electronic signature service.
Without the implementation of an online signature software, you will end up exposing your company to legal troubles. One of the top electronic signature software that are used by organizations commonly is called DocuSign.
Click to the Image to Check Price
It is available at a nominal fee per month and ends up being a low-risk method to send a couple of documents per month while also getting an understanding of how online document signing actually operates.
As said, DocuSign is not the only choice that there is for an online signature software.
However, I’m just diverting your attention with this statement, DocuSign competition is a burning issue in the electronic signature software industry. Each DocuSign competitor has been trying to beat it by facilitating the user’s with same features in less budget.
Furthermore, all the DocuSign competitors that have been mentioned below provide free trial with some features. You can utilize for a days, if you are not satisfied with them. You can easily switch it and try another alternative to DocuSign.
There are multiple competitors and alternatives to DocuSign that are available in the market and they make a great choice. In this blog, I have listed top DocuSign competitors and alternatives that can make your selection convenient.
Let we start understanding of free DocuSign alternative one by one. Each DocuSign Competitor’s pricing have denoted based on the basic features that they offer. You may find out more about business and enterprise planning by visiting the software’s website.
➽ SignNow — 1st Free DocuSign Competitor & Alternative
➽ CEO — Borya Shakhnovich
➽ Mobile App: iOS | Android
➽ Location — Brookline, Massachusetts
In the list of DocuSign’s competitors, SignNow is one of the top Docusign competitor and electronic signature software for small businesses that comes with features needed to sign and send documents. It helps in generating agreements, automating and streamlining processes, accessing payments, and managing documents.
This application has reusable templates that help in simplifying the process of sending documents and saving time. When it comes to workflows, SignNow allows you to organize documents into groups and send them based on the roles of receivers.
With SignNow, it is also possible to set different actions after signing has been completed.
➽ Wesignature — Free DocuSign Competitor & Alternative
➽ CEO — Ryan Pegram
➽ Mobile App: None, web-based only
➽ Location — Thornton, Colorado
WeSignature is one of the best document signing software and most widely chosen electronic signature software at the present time.
Many professionals have been using this application for their personal and professional use which makes it the best DocuSign competitor.
It is a simple, efficient, and effortless software for signing documents as it enables individuals and organizations to sign a wide range of online documents.
Once you adopt the WeSignature application, you can sign the documents, fill up paperwork, and follow up with the receivers regularly.
It is an application that has consistently proven itself to be the best electronic signature service for small businesses.
Once you start using WeSignature, you will be surprised at the reduction of turnaround time from a couple of days to just a few minutes.
In addition, it also enables the organizations to send multiple documents to people all at once.
➽ Signaturely — Free DocuSign Competitor & Alternative
➽ CEO — Will Cannon
➽ Mobile App: None, web-based only
➽ Location — 340 S Lemon Ave Ste 1760 Walnut, CA 91789
In another competitors to DocuSign, Signaturely is well known esignature software. It is also preferred by many people who are looking for simple methods to get their documents signed in a legal manner. Signaturely has proven itself to be a great alternative to DocuSign because of its simplicity.
Signaturely is easy to use and also makes online document signing simple. The reason why Signaturely stands out is that it focuses on eliminating the features.
It lays special emphasis on cutting down all unnecessary steps so that it becomes easy to get your documents signed.
➽ CocoSign — Free DocuSign Competitor & Alternative
➽ CEO — Stephen Curry
➽ Mobile App: None, web-based only
➽ Location — Singapore
Over the past few years, CocoSign has emerged as an extremely renowned online signature platform. It is one of best DocuSign competitor and is used for sending, signing, saving, and accessing documents online.
It is capable of automating business processes by closing deals quickly, safely, and legally.
CocoSign enables users to choose a free trial for understanding how the platform should work and how useful it can be. It is easily the best place for online signatures as it improves businesses by automating significant parts of business deals. It is empowered with multiple applications, integrations, APIs, and industry-specific solutions.
CocoSign allows you to get signatures digitally without facing problems in managing paperwork. It provides a user-friendly, digital, and integrated experience for creating e-signatures.
In addition, it also offers cross-platform functionality and can be accessed anywhere. People use it because it is safe, legally compliant, and efficient
➽ HelloSign — Free DocuSign Competitor & Alternative
➽ CEO — Joseph Walla
➽ Mobile App: None, web-based only
➽ Location — San Francisco, CA
HelloSign is another online electronic signature software that is also understood to bring a wide range of features to the market. It is great with customer service, customization, and flexible pricing as well.
It also comes with a great API that enables you to embed and brand the signing options in the online documents.
This is an electronic signature company that is also compliant with all of the major online signature laws while offering an array of extensions and integrations.
It is an application owned by Dropbox and comes with powerful integration along with many tools such as Google Suite, Gmail, and more.
➽ Adobe Sign — Free DocuSign Competitor & Alternative
➽ CEO — Shantanu Narayen
➽ Mobile App: iOS | Android
➽ Location — San Jose, CA
Adobe Sign is a feature-rich among DocuSign competitors. It is an online signature platform that provides you with the power to manage the workflows from any location and device. Many people use this app because of the seamlessness that it is capable of offering in electronic document signing.
Adobe Sign is an application that is known for its wide integration with third-party tools along with an added focus on global compliance.
It is full of features for both electronic and digital signatures. Many professionals have been choosing Adobe Sign for their personal and professional use.
➽ PandaDoc — Free DocuSign Competitor & Alternative
➽ CEO — Mikita Mikado
➽ Mobile App: iOS | Android
➽ Location — San Francisco, California
Yet another top competitors to DocuSign is PandaDoc which is very well known for offering a streamlined user interface and ease of use.
This is an online signature tool that is known for providing a streamlined user interface and ease of use.
It is an e-signature tool that comes with great assistance in document management.
PandaDoc comes with a drag and drops integration, automated workflow, and audit history as well. It has multiple integrations including CRM, file storage applications, and payments.
If you are looking for an effective solution for the management of contracts then PandaDoc is worth giving a shot.
➽ RightSignature — Free DocuSign Competitor & Alternative
➽ CEO — Daryl Bernstein, Cary Dunn, and Jonathan Siegel
➽ Mobile App: None, web-based only
➽ Location — Fort Lauderdale, FL
Next DocuSign competitor, RightSignature is a perfect alternative to DocuSign that comes with a wide range of integrations as an important part of the e-signature process. It specializes in making document signing a simple process.
The users can upload online documents to RightSignature with a drag and drop tool for placing signature fields inside the document.
Once this happens, the users can send the document through an email to the customer for an optimized online signing experience. Using RightSignature offers plans for individuals and enterprise-level users as well. It provides features that are completely different as is the cost.
RightSignature also treats uploaded documents such as a locked PDF while enabling the users to drag the signature fields on the top of the page.
The custom branding with RightSignature is more like a white labeling feature as opposed to a branding kit.
➽ SignWell — Free DocuSign Competitor & Alternative
➽ CEO — Martin Holmstrom
➽ Mobile App: None, web-based only
➽ Location — Portland, Oregon
SignWell is another one of the competitors to DocuSign that is a cost-effective and user-friendly electronic signature application used by many businesses. It helps in eliminating many hours from the usual document signing process and is also compliant with e-signature laws.
This application comes with a free plan that also includes various features such as document tracking, flexible workflows, and reminders. This is an application that is being used by a wide range of people in recent times.
➽ SignEasy — Free DocuSign Competitor & Alternative
➽ CEO — Sunil Patro
➽ Mobile App: iOS | Android
➽ Location — Brookline, Massachusetts
SignEasy is yet another top recommendation for many people. It is one of the best electronic signing software for personal use. You can sign up with a free trial and you can instantly begin by uploading documents, preparing them for signatures, and sending them.
SignEasy comes with wide integration support and also works within your favorite applications. You can open a document with Gmail, sign it, and then send it without any stress.
Finally, you can also take benefit of many features such as automated reminders, tracking, and signing sequences.
➽ Eversign — Free DocuSign Competitor & Alternative
➽ CEO — Julian Zehetmayr
➽ Mobile App: None, web-based only
➽ Location — Wien, Wien
The another choice in the list for DocuSign competitors is Eversign. It is a great solution for all users who need legally binding online signatures but are not looking to break the bank with a high fee.
Eversign is a cost-effective choice that comes with the ability to send many documents per month without an added fee.
The basic features that Eversign offers are all included in audit trails, contract management, and app integrations.
The businesses that are looking to onboard more users or seeking additional perks such as in-person signing can exist without any extra price tag.
➽ DigiSigner — Free DocuSign Competitor & Alternative
➽ CEO — Jessica Kelly
➽ Mobile App: None, web-based only
➽ Location — 700 N Valley St Suite B Anaheim, CA 92801
DigiSigner is a cloud-based electronic signature software and one of the best DocuSign competitor that focuses on speed, affordability, and convenience of use.
Using the service, businesses and people can sign contracts and agreements from any location in the world, regardless of their location.
DigiSigner is compatible with a wide range of devices, including laptops, tablets, smartphones, and more.
All main e-signature laws, such as ESIGN, UETA, and European eIDAS, are met by DigiSigner.
DigiSigner’s signatures are legally binding and can be used in a court of law.
➽ SIGNiX — Free DocuSign Competitor & Alternative
➽ CEO — Jay Jumper
➽ Mobile App: None, web-based only
➽ Location — Chattanooga, TN
A next DocuSign competitor is SIGNiX, which makes it easy for partners in highly regulated industries like real estate, wealth management, and healthcare to use digital signature and online notarization software together.
There are no costs or risks to using the patented SIGNiX FLEX API.
It allows partners to offer military-grade cryptography, enhanced privacy, and permanent legal evidence of a true digital signature without having to deal with paper-based processes.
➽ Scrive — Free DocuSign Competitor & Alternative
➽ CEO — Viktor Wrede
➽ Mobile App: None, web-based only
➽ Location — Stockholm, Stockholm County
As soon as Scrive was started in 2010, it quickly became a leader in the Nordic e-sign market and take place in the list of top DocuSign competitors.
Today, more than 6000 customers in 40+ countries use Scrive to speed up their onboarding and agreements processes with solutions that use electronic signatures and IDs.
As a trusted digitalization partner, Scrive helps businesses of all sizes, even those in highly regulated industries, move forward with their digital transformations.
This includes improving customer experience, security, compliance, and data quality. Scrive is based in Stockholm and is owned by Vitruvian Partners. It has more than 200 employees.
➽ Secured Signing Software — Free DocuSign Competitor & Alternative
➽ CEO — Gal Thompson
➽ Mobile App: None, web-based only
➽ Location — 800 W. El Camino Real, Suite 180 Mountain View CA 94040
Secured Signing Software is a cloud-based service for managing electronic signatures. It works with businesses of all sizes in a wide range of industries, including finance, education, and real estate.
Users can sign documents digitally, send email invitations to complete documents, and make forms with the help of these tools.
Secured Signing lets people send invitations to a group of people. Businesses can set up reminders and add extra fields to documents to help them.
The solution lets users add electronic signatures to word documents and set up approval processes. It may also let recipients add or change text before they sign.
Secured Signing works with Salesforce, Realme, and Microsoft Dynamics 365. Face-to-face signing lets customers use an SMS code to prove their identity and then sign on the screen.
In the dashboard, users can see how long it will take to sign a form, as well as download and store signed copies of the form.
If you want to use Secured Signing’s services, you can pay for them each month or pay as you go.
Customer service is available through an online ticketing system, an online knowledge base, phone, and email, among other ways.
➽ eSign Genie — Free DocuSign Competitor & Alternative
➽ CEO — Mahender Bist
➽ Mobile App: None, web-based only
➽ Location — Cupertino, California
This electronic document signing service is actually quite expensive, despite the fact that it appears to be a bargain at first glance. Esign Genie is a splendid choice among top notch DocuSign competitors, despite its low price, is packed with capabilities that make the e-signing process more convenient for both signers and organizations.
The process of connecting to eSign Genie is as simple as creating a network connection and using the form-signing tools.
eSign Genie can help you collect document signing for a fraction of the cost of competing products like PandaDoc and GetAccept.
When it comes to the business, pay-as-you-go is one of the most intriguing aspects. This package also includes the ability to sign papers in person and assign signers, two features that are often reserved for more expensive e-signature services.
➽ SignRequest — Free DocuSign Competitor & Alternative
➽ CEO — Geert-Jan Persoon
➽ Mobile App: None, web-based only
➽ Location — Amsterdam, Noord-Holland
SignRequest appears to offer a lot of capability and customizability for senders who need to transmit multiple papers each month. When creating papers for multiple signers, the professional plan comes with features including a post-signature landing page and the ability to alter the document signing sequence.
This platform is ideal for small-business owners who don’t need to transmit a lot of documentation each month, and it offers everything you need.
You can easily see what paperwork has yet to be completed and what documentation has already been finished thanks to SignRequest’s document management features.
When it comes to creating templates, collecting signer attachments, and selecting the authentication mechanism your signatures may use, SignRequest is the best document signing software for businesses or anyone searching for a quick and easy method to sign.
➽ DrySign — Free DocuSign Competitor & Alternative
➽ CEO — Ron Cogburn
➽ Mobile App: None, web-based only
➽ Location — 2701 E. Grauwyler Road Irving, TX 75061, USA
Due to its cloud-based best electronic signing software for company, it may assist speed up internal and external sign-offs, reduce the need for paper procedures, and increase team efficiency.
It is possible to use DrySign with any of the following cloud storage services: Google Drive, Dropbox, OneDrive, and CRM. It can be utilized on a variety of platforms, including PCs, laptops, and even handheld devices.
The ESIGN Act and the UETA are among the electronic signing regulations that this method complies with. DrySign provides an audit trail, multi-factor authentication, and smart tracking to assist businesses reduce the risk connected with their transactions.
All documents and activity can be viewed from the dashboard. Multiple signatures can be requested, automated notifications can be set up, changes can be viewed in real time, document fields can be altered, and bulk files can be uploaded.
➽ KeepSolid Sign — Free DocuSign Competitor & Alternative
➽ CEO — Vasiliy Ivanov
➽ Mobile App: None, web-based only
➽ Location — Bronx, New York
Various documents, such as contracts, transactions, and other agreements, can be signed electronically with KeepSolid Sign. In either case, it is possible to set up this product.
As compared to DocuSign, KeepSolid Sign allows you to sync documents between several platforms, including PCs, tablets, and cellphones. The data in the system is encrypted with AES-256. Apps for iOS and Android devices are also available from the company.
Signing and annotating documents without being online is feasible, and the changes you make are saved. In order to keep track of the progress of a document, the service provides an activity dashboard that may be used by the user.
➽ Formstack Sign — Free DocuSign Competitor & Alternative
➽ CEO — Chris Byers
➽ Mobile App: iOS | Android
➽ Location — Fishers, IN
In the DocuSign competition, Formstack Sign is an e-sign document platform built inside the Formstack data management system, allows users to sign documents electronically.
Filling out surveys and applying for jobs are just two examples of online forms that Formstack Sign is well-suited to handle because of its accessibility and signature automation features.
It is possible to trace the origins of the company back to Ade Olonoh, who founded FormSpring on February 28th, 2006. FormSpring predated FormStack.
Initially, it was designed to be an online form builder that also provided workflow management solutions for businesses like higher education and marketing.
An added benefit is that the integration of more than 50 web applications such as customer relationship management (CRM), email marketing (email marketing), payment processing and document management is available to Formstack customers who don’t have programming or software abilities.
➽ ZorroSign — Free DocuSign Competitor & Alternative
➽ CEO — Shamsh Hadi
➽ Mobile App: iOS | Android
➽ Location — Phoenix, AZ
Document-based transactions, such as payroll and employee onboarding, can be managed with ZorroSign. It is a cloud-based electronic signature and digital transaction management solution.
ZorroSign uses proprietary forensics technologies to identify document forgery and signature forgery based on Blockchain technology.
If you’re looking for a document signing software that’s more secure than some of DocuSign alternative, such as government, legal, or healthcare, this is an excellent option. ZorroSign is also an environmentally responsible company as compared to other DocuSign Competitors.
➽ PDF Filler — Free DocuSign Competitor & Alternative
➽ CEO — Boris Shakhnovich
➽ Mobile App: iOS | Android
➽ Location —Brookline, Massachusetts
For editing, producing, signing, and maintaining PDF files online, pdfFiller is the best option available. It is one of the greatest alternative to DocuSign.
Using it since 2008, it has made it easier for companies and people to go paperless!
Additionally, pdfFiller is part of the airSlate Business Cloud, a simple bundle that offers a variety of useful services.
➽ Dochub — Free DocuSign Competitor & Alternative
➽ CEO — Chris Devor
➽ Mobile App: None, web-based only
➽ Location — Boston
Online PDF annotation and best electronic signing software, Dochub, allows users to add text and photos to their PDFs online. Multi-signer procedures, document signing in bulk, lossless editing, team collaboration, and more are all possible with this program.
The low cost of this program makes it a standout in comparison to DocuSign. The process of setting up and transmitting documents to users has been made simpler.
With DocHub’s editing features and the option to keep multiple signatures on several devices, it’s easy to collaborate with others. As a result of this functionality, it is a significant DocuSign competitor.
➽ EasySIGN— Free DocuSign Competitor & Alternative
➽ CEO — Not Found
➽ Mobile App: None, web-based only
➽ Location — Hapert, The Netherlands
They are proud to say that EasySIGN is acknowledged as a real one-stop-shop software solution for the signmaking and digital big format printing businesses worldwide.
The software provides unique, non-destructive design and production capabilities that are both inventive and easy to use in order to convert creative ideas into production-ready realities.
It offers a worldwide network of resellers that are experts in the field. More than 25 nations use the program, which has been translated into numerous languages and delivered to a dedicated clientele.
➽ Signority— Free DocuSign Competitor & Alternative
➽ CEO —Jane He
➽ Mobile App: None, web-based only
➽ Location — ON Canada
When you use Signority, the eSignature process is completely automated and your document management costs are much reduced. This allows you to focus more on your business.
Electronic signatures and reminders are both possible with the software, which delivers papers for digital signature and eSignature. All of your papers may be securely shared and kept in the cloud after they have been safely changed.
It enables process automation, corporate branding, real-time status alerts and traceability, and a host of additional benefits.
➽ Contract Book — Free DocuSign Competitor & Alternative
➽ CEO —Niels Martin Brochner
➽ Mobile App: None, web-based only
➽ Location — New York, USA
Contractbook is a collaborative contract management platform that automates your process and syncs contract data across your corporate platforms. It is also a good document signing software as well as DocuSign competitor.
You may use it to handle contracts effectively. All forms of legal papers may be signed, created, and stored digitally with this program. It also aids in the creation of a more open company environment.
Compliance is guaranteed and time is saved thanks to the software Using the solution, legal practitioners may monitor and manage their client’s contract online with ease and security.
➽ Proposify — Free DocuSign Competitor & Alternative
➽ CEO — Kyle Racki
➽ Mobile App: None, web-based only
➽ Location — Halifax, Nova Scotia
It’s a web-based proposal management system. By using Proposify, you can take charge of and gain insight into the most important portion of the sales process. Develop your self-assurance and flexibility to the point where you can dictate the terms of any business transaction.
Produce flawless and consistent sales materials. Get the info you need to expand your process, make appropriate commitments, and develop accurate forecasts. Streamline the approval procedure for your clients and new clients.
This competitor to DocuSign offers a wide variety of features, including an easy-to-use design editor, electronic signatures, CRM integration, data-driven insights, dynamic pricing and packaging, document management, approval workflows, and more.
➽ Qwilr — Free DocuSign Competitor & Alternative
➽ CEO — Dylan Baskind
➽ Mobile App: None, web-based only
➽ Location — Redfern, New South Wales
Qwilr intends to completely alter the online paper-making and distribution processes for corporations. With this innovation, companies can easily convert static web material into interactive, user-friendly mobile experiences. According to the vendor, this enables businesses to provide clients with quotes, proposals, and presentations while also taking advantage of analytics and other capabilities.
Increase your company’s sales output by freeing your sales staff from mundane duties like copying and pasting and allowing them to focus on more strategic initiatives. Build a database of documents that can be readily updated when you receive new contacts. Effective real-time collaboration is made simpler with intuitive commenting and discussion features.
Provide a quote that can be modified in real time, signed digitally, and used in other ways to meet the individual requirements of each client. Without leaving the Qwilr app, your clients can give final approval by checking boxes and providing comments.
Qwilr, like other DocuSign competitors, is a web-based document editor that could help your company save time and look more professional when communicating with customers.
➽ DealHub — Free DocuSign Competitor & Alternative
➽ CEO —Eyal Elbahary
➽ Mobile App: None, web-based only
➽ Location — Austin, Texas
The next notable DocuSign competitor is DealHub, which offers your business the most complete and unified revenue workflow available. Our zero-code platform is designed specifically to aid visionary leaders in improving cooperation, speeding up the sales cycle, and maintaining a steady flow of leads into the pipeline.
With the support of CPQ, CLM, and Subscription Management tools, which are all driven by an intuitive Sales Playbook, you can speed up the contract negotiation process, improve content delivery, and clinch more deals. A digital DealRoom is a platform where buyers and sellers can connect and exchange information in one convenient location.
Market leaders such as WalkMe, Gong, Drift, Hopin, Yotpo, Sendoso, and Braze are using DealHub to reduce their time to revenue and deliver a consistent sales experience for their sales teams and customers.
➽ DocSend— Free DocuSign Competitor & Alternative
➽ CEO — Russ Heddleston
➽ Mobile App: None, web-based only
➽ Location —San Francisco, CA
Sharing and managing the information that drives your business forward has never been easier than with Dropbox DocSend. Dropbox DocSend’s link-based approach makes it simple to tailor security to each recipient, track file views in real-time, evaluate content performance on a page-by-page basis, and establish up state-of-the-art virtual deal rooms. DocSend, like DocuSign’s competitors, is an e-signature service that may be used by any company.
The above-mentioned are some of the best DocuSign competitors that can help you to figure out which option suits your needs in the best ways. Many online signature software that have been discussed offer a free version so that the customers can find an easy and perfect solution.
If you ask our opinion then Signnow and WeSignature is the best option. It is the best one of the top online electronic signature software that come with a wide range of features. You can use this software to send unlimited documents while professionally designing documents for any purpose and numerous integrations.
You can begin a free trial and see how SignNow and WeSignature can help.
If you’re only searching for a way to submit and sign your electronic papers, DocuSign is fantastic. However, teams who wish to do more with their documents will find it to be a poor fit.
Here are some of the reasons why you might want to look at one of DocuSign’s competitors.
The market for electronic signatures is constantly changing.
Other significant e-signing hubs including SignNow, Wesignature, Signaturely, and PandaDoc are currently greatest DocuSign’s competitors.
On the market, there are several eSignature service providers, all of them provide slightly unique products. It can be difficult to decide which eSignature solutions will be most useful for you and which could be a waste of money, though, given the abundance of options. Prior to deciding on a DocuSign competitors for your company, consider the following points:
Finding the ideal DocuSign competitors for you will be much simpler now that you are aware of the answers to those concerns.
There are cheaper competitors to DocuSign, and SignNow is one of them. If your company needs an easy way to collect valid electronic signatures, look no further than SignNow eSignature software.
The excellent DocuSign competitor is SignNow. When you use SignNow to send out documents for signature, you may send a lot more of them at a much reduced cost. Use it more easily and get better customer service.
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Straight-up document scanning, basic connectivity
I focus on printer and scanner technology and reviews. I have been writing about computer technology since well before the advent of the internet. I have authored or co-authored 20 books—including titles in the popular Bible, Secrets, and For Dummies series—on digital design and desktop publishing software applications. My published expertise in those areas includes Adobe Acrobat, Adobe Photoshop, and QuarkXPress, as well as prepress imaging technology. (Over my long career, though, I have covered many aspects of IT.)
Brother's entry-level ADS-3100 is a capable sheetfed document scanner that's a good value for home, hybrid, or small offices and workgroups.
At the bottom of the pecking order in Brother’s recent release of a wave of sheetfed desktop document scanners, its ADS-3100 High-Speed Desktop Scanner ($329.99) is relatively fast and reliably accurate. It lists for about $40 less than the next model up, the ADS-3300W, but you give up quite a lot for the savings: network connectivity, a touch-screen control panel, and support for smartphones and other handheld devices, to name a few convenience and productivity features. That’s not to say, however, that small or home offices that plug the ADS-3100 into a solitary computer’s USB port (or scan directly to a USB flash, solid-state, or hard drive) won’t get good value from this scanner. Competition among entry-level and mid-volume document scanners is formidable, but if your business doesn’t demand networkability or wireless scanning, this Brother model should serve you well.
The ADS-3100 is the last of five new sheetfed document scanners from Brother to reach our test bench. The ADS-4900W is the high-volume flagship (and an Editors’ Choice award winner), and the midrange ADS-4700W, ADS-4300N, and ADS-3300W are excellent as well. They are all the same size. The five Brothers measure 7.5 by 11.7 by 8.5 inches (HWD) with their paper trays closed—like most scanners in this category, they double or triple their desktop footprint with trays extended for use—and weigh 6.2 to 6.5 pounds each.
This scanner has too many direct competitors to list here, so we’ll name just four key ones: the Fujitsu ScanSnap iX1400, the Epson WorkForce ES-400 II, the Raven Select Document Scanner, and the Canon imageFormula R50 Office Docment Scanner. The ADS-3100 falls short of some rivals by lacking a color touch screen. Its control panel holds only four buttons (Power, Stop/Cancel, Scan to USB, Scan to PC) and a few LED status indicators. This and the ADS-4300N are the only members of the new Brother quintet to lack touch screens.
You can scan to a variety of PDF types (high-compression, image, searchable, secure, or signed), as well as PDF/A, single-page and multipage TIFF, BMP, plain text, and Microsoft Word, Excel, and PowerPoint formats. The scanner’s maximum resolution is 600 dots per inch (1,200dpi interpolated), and it supports document sizes ranging from 2 inches square to 8.5 inches wide by 16.4 feet long, with 24-bit color depth. 
Online scanning destinations include cloud and social media sites and FTP sites, as well as local drives and email. It’s easy to access most social media and cloud sites, though the ADS-3100 is preconfigured to support Google Drive, OneDrive, Evernote, Box, Dropbox, OneNote, SharePoint Online, and Expensify.
The ADS-3100 features a 60-sheet automatic document feeder (ADF) for sending single- and double-sided multipage documents to the scanner. Brother rates its daily duty cycle at 6,000 scans. These specs match the ADS-4300N’s; the ADS-4700W has a larger 80-page ADF, while the more robust ADS-4900W combines a 100-sheet feeder and 9,000-scan daily duty cycle.
These specs are more or less average among low-end document scanners. The Raven Select, Epson ES-400 II, and ScanSnap iX1400 all have 50-sheet ADFs, while the Canon R50’s holds 60 sheets. The Fujitsu has the same 6,000-scan duty rating as the Brother; the Epson and Canon are rated for 4,000 and the Raven for only 2,000 daily scans.
Of the five new Brother document scanners, the ADS-3100 is the only one without a wired or wireless network interface—its sole connectivity option, apart from the USB Type-A port around back for scanning directly to storage devices, is a USB 3.0 (or 2.0) cable. That leaves out smartphones and tablets.
The software bundle doesn’t reflect this printer’s lower-end status, though. It includes Brother iPrint&Scan (desktop) for Windows and Mac, Brother ScanEssentials Lite for Windows, Kofax Power PDF for Windows, Presto! BizCard for Windows and Mac, Image Folio Processing Software for Windows, and Kofax PaperPort SE with OCR for Windows.
iPrint&Scan is an all-in-one printer interface that’s also compatible with Brother’s single-function scanners and printers. It lets you create and manage workflow profiles that you can choose with the front-panel buttons.
ScanEssentials Lite, meanwhile, is a trimmed-down version of another Brother scanner interface that’s also a document-management and financial-data archiving application. Kofax PaperPort also combines a scanner interface with document-management features, among them its own optical character recognition (OCR), workflow profiles, and automated naming conventions.
Presto! BizCard is what it sounds like, a business-card-scanning and contact-archiving program, and Image Folio is an image-capturing program designed to help you scan, edit, enhance, and print photos. You also get four third-party drivers—TWAIN, WIA, ISIS, and Sane—for scanning directly into many compatible applications, such as Adobe Acrobat and Photoshop and the Microsoft 365 suite.
Like its siblings (barring the flagship ADS-4900W), the ADS-3100 is rated at 40 one-sided (simplex) pages per minute (ppm) and 80 two-sided (duplex) images per minute (ipm, where each page side is counted as an image). I put those speed figures to the test using iPrint&Scan over a USB connection to our Intel Core i5 testbed running Windows 10 Pro. (I also ran a few tests with some of the other apps and got similar results, though scanning to USB flash drives is notably faster; see more about how we test scanners.)
First, I clocked the ADS-3100 as it scanned our standard one-sided 25-page and two-sided 25-page (50 sides) documents and then converted and saved them as image PDF files. The scanner managed 41.6ppm and 81.3ipm, barely beating its ratings. The competitors mentioned here did similarly, with the ADS-4900W being faster (68.7ppm and 125.4ipm) and the Epson ES-400 II being slower (37.7ppm and 66.7ipm).
Next, I timed the Brother as it scanned our two-sided, 25-page hard-copy document and saved it to the more versatile searchable PDF format. The ADS-3100 finished the job in 38 seconds, on the high side of average. The ADS-4900W and the HP ScanJet Pro N4000 snw1 took 34 seconds and 24 seconds respectively, while the Canon R50 took 37 seconds. The Fujitsu made it in 40 seconds, versus 41 for the Raven Select and 44 for the Epson. Frankly, unless you spend most of your day scanning stacks of lengthy documents, these scores should be fast enough for most offices.
Besides, how well a scanner reads text is more important than how quickly it does so. Most document scanners today can convert printed text to searchable PDF format with no errors down to 5 or 6 points. The Brother ADS-3100 was perfectly average, managing scans down to 6-point type problem-free in both our Arial and Times New Roman font tests. Of the other machines discussed here, only the Canon R50 yielded a different result: down to 5 points for Arial, and 6 points for Times New Roman. You’re not likely to encounter text smaller than 10 points in most real-world business documents.
I also scanned a few stacks of business cards into Presto! BizCard, with predictable results: The software does a fine job of digitizing text and figures and putting them into the proper fields in a contacts database (or exporting them to Outlook, Gmail, and other contact management or personal information manager apps). Finally, to see how well the Brother handled images, I scanned several photos to Image Folio. Most were well-detailed, with bright and accurate colors, showing the ADS-3100 can serve as a decent sheetfed photo scanner or alternative to the Epson FastFoto FF-680W and Canon imageFormula RS40. If you have a shoebox of photos stashed under the bed or on a closet shelf, the Image Folio software should deliver additional value.
If you can live without networked or mobile scanning, the Brother ADS-3100 may be right for you, especially if you also have a bunch of photos to archive (though it’s more of a document than a photo scanner). Without wireless or Ethernet connectivity, however, it’s really more of a personal machine for relatively low scanning volumes, around a few hundred per day. (You’d have to fill its 60-sheet ADF 100 times per day, or 14 times per hour, to reach its 6,000-scan limit.) Under the right conditions, though, the ADS-3100 is without question a fine entry-level-to-midrange document scanner.
Brother's entry-level ADS-3100 is a capable sheetfed document scanner that's a good value for home, hybrid, or small offices and workgroups.
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I focus on printer and scanner technology and reviews. I have been writing about computer technology since well before the advent of the internet. I have authored or co-authored 20 books—including titles in the popular Bible, Secrets, and For Dummies series—on digital design and desktop publishing software applications. My published expertise in those areas includes Adobe Acrobat, Adobe Photoshop, and QuarkXPress, as well as prepress imaging technology. (Over my long career, though, I have covered many aspects of IT.)
In addition to writing hundreds of articles for PCMag, over the years I have also written for many other computer and business publications, among them Computer Shopper, Digital Trends, MacUser, PC World, The Wirecutter, and Windows Magazine. I also served as the Printers and Scanners Expert at About.com (now Lifewire).
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The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics.
The accelerating use of artificial intelligence (AI) and machine learning (ML) technologies in systems of software, hardware, data, and people introduce vulnerabilities and risks due to dynamic and unreliable behaviors; fundamentally, ML systems learn from data, introducing known and unknown challenges in how these systems behave and interact with their environment. Currently, the approach to building AI technologies is siloed: models and algorithms are developed in testbeds isolated from real-world environments, and without the context of larger systems or broader products they will be integrated within for deployment. The main concern is models are typically trained and tested on only a handful of curated datasets, without measures and safeguards for future scenarios, and oblivious of the downstream tasks and users. Even more, models and algorithms are often integrated into a software stack without regard for the inherent stochasticity and failure modes of the hidden ML components. Consider the massive effect random seeds have on deep reinforcement learning model performance¹, for instance.
Other domains of engineering, such as civil and aerospace, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. Technology Readiness Level (TRL) is a systems engineering protocol for deep tech² and scientific endeavors at scale, ideal for integrating many interdependent components and cross-functional teams of people. It is no surprise that TRL is a standard process and parlance in NASA³ and DARPA⁴.
For a spaceflight project, there are several defined phases, from pre-concept to prototyping to deployed operations to end-of-life, each with a series of exacting development cycles and reviews. This is in stark contrast to common machine learning and software workflows, which promote quick iteration, rapid deployment, and simple linear progressions. Yet the NASA technology readiness process for spacecraft systems is overkill; we need robust ML technologies integrated with larger systems of software, hardware, data, and humans, but not necessarily for missions to Mars. We aim to bring systems engineering to AI and ML by defining and putting into action a lean Machine Learning Technology Readiness Levels (MLTRL) framework. We draw on decades of AI and ML development, from research through production, across domains and diverse data scenarios: for example, computer vision in medical diagnostics and consumer apps, automation in self-driving vehicles and factory robotics, tools for scientific discovery and causal inference, streaming time-series in predictive maintenance and finance.
In this paper, we define our framework for developing and deploying robust, reliable, and responsible ML and data systems, with several real test cases of advancing models and algorithms from R&D through productization and deployment, including essential data considerations—Fig. 1 illustrates the overall MLTRL process. Additionally, MLTRL prioritizes the role of AI ethics and fairness, and our systems AI approach can help curb the large societal issues that can result from poorly deployed and maintained AI and ML technologies, such as the automation of systemic human bias, denial of individual autonomy, and unjustifiable outcomes (see the Alan Turing Institute Report on Ethical AI⁵). The adoption and proliferation of MLTRL provide a common nomenclature and metric across teams and industries. The standardization of MLTRL across the AI industry should help teams and organizations develop principled, safe, and trusted technologies.
Most ML workflows prescribe an isolated, linear process of data processing, training, testing, and serving a model³⁷. Those workflows fail to define how ML development must iterate over that basic process to become more mature and robust, and how to integrate with a much larger system of software, hardware, data, and people. Not to mention MLTRL continues beyond deployment: monitoring and feedback cycles are important for continuous reliability and improvement over the product lifetime.
MLTRL defines technology readiness levels (TRLs) to guide and communicate machine learning and artificial intelligence (ML/AI) development and deployment. A TRL represents the maturity of a model or algorithm, data pipelines, software module, or composition thereof; a typical ML system consists of many interconnected subsystems and components, and the TRL of the system is the lowest level of its constituent parts⁶. Note we use “model” and “algorithm” somewhat interchangeably when referring to the technology under development. The same MLTRL process and methods apply for a machine translation model and for an A/B testing algorithm, for example. The anatomy of a level is marked by gated reviews, evolving working groups, requirements documentation with risk calculations, progressive code and testing standards, and deliverables such as TRL Cards (Fig. 2) and ethics checklists. Templates and examples for MLTRL deliverables will be open-sourced upon publication at ai-infrastructure.org/mltrl. These components—which are crucial for implementing the levels in a systematic fashion—as well as MLTRL metrics and methods are concretely described in examples and in the “Methods” section. Lastly, to emphasize the importance of data tasks in ML, from data curation⁷ to data governance⁸, we state several important data considerations at each MLTRL level.
Here is an example reflecting a neuropathology machine vision use-case²², detailed in the “Discussion” section. Note this is a subset of a full TRL Card, which in reality lives as a full document in an internal wiki. Notice the card clearly communicates the data sources, versions, and assumptions. This helps mitigate invalid assumptions about performance and generalizability when moving from R&D to production and promotes the use of real-world data earlier in the project lifecycle. We recommend documenting datasets thoroughly with semantic versioning and tools such as datasheets for datasets⁷⁶, and following data accountability best practices as they evolve (see ref. 81).
The levels are briefly defined as follows and in Fig. 1, and elucidated with real-world examples later.
This is a stage for greenfield AI research, initiated with a novel idea, guiding question, or poking at a problem from new angles. The work mainly consists of literature review, building mathematical foundations, white-boarding concepts, and algorithms, and building an understanding of the data—for work in theoretical AI and ML, however, there will not yet be data to work with (for example, a novel algorithm for Bayesian optimization⁹, which could eventually be used for many domains and datasets). The outcome of Level 0 is a set of concrete ideas with sound mathematical formulation, to pursue through low-level experimentation in the next stage. When relevant, this level expects conclusions about data readiness, including strategies for getting the data to be suitable for the specific ML task. To graduate, the basic principles, hypotheses, data readiness, and research plans need to be stated, referencing relevant literature. With graduation, a TRL Card should be started to succinctly document the methods and insights thus far—this key MLTRL deliverable is detailed in the “Methods” section and Fig. 2.
Level 0 data—Not a hard requirement at this stage as it is largely relevant to theoretical machine learning. That being said, data availability needs to be considered for defining any research project to move past theory.
Level 0 review—The reviewer here is solely the lead of the research lab or team, for instance, a Ph.D. supervisor. We assess hypotheses and explorations for mathematical validity and potential novelty or utility, not necessarily code nor end-to-end experiment results.
To progress from basic principles to practical use, we design and run low-level experiments to analyze specific model or algorithm properties (rather than end-to-end runs for a performance benchmark score). This involves the collection and processing of sample data to train and evaluate the model. This sample data need not be the full data; it may be a smaller sample that is currently available or more convenient to collect. In some cases it may suffice to use synthetic data as the representative sample—in the medical domain, for example, acquiring datasets can take many months due to security and privacy constraints, so generating sample data can mitigate this blocker from early ML development. Further, working with the sample data provides a blueprint for the data collection and processing pipeline (including answering whether it is even possible to collect all necessary data), that can be scaled up for the next steps. The experiments, good results or not, and mathematical foundations need to pass a review process with fellow researchers before graduating to Level 2. The application is still speculative, but through comparison studies and analyses, we start to understand if/how/where the technology offers potential improvements and utility. Code is research-caliber: The aim here is to be quick and dirty, moving fast through iterations of experiments. Hacky code is okay, and full test coverage is actually discouraged, as long as the overall codebase is organized and maintainable. It is important to start semantic versioning practices early in the project lifecycle, which should cover code, models, and datasets. This is crucial for retrospectives and reproducibility, issues that can be costly and severe at later stages. This versioning information and additional progress should be reported on the TRL Card (see, for example, Fig. 2).
Level 1 data—At minimum, we work with sample data that is representative of downstream real datasets, which can be a subset of real data, synthetic data, or both. Beyond driving low-level ML experiments, the sample data forces us to consider data acquisition and processing strategies at an early stage before it becomes a blocker later.
Level 1 review—The panel for this gated review is entirely members of the research team, reviewing for scientific rigor in early experimentation, and pointing to important concepts and prior work from their respective areas of expertise. There may be several iterations of feedback and additional experiments.
Active R&D is initiated, mainly by developing and running in testbeds: simulated environments and/or simulated data that closely matches the conditions and data of real scenarios—note these are driven by model-specific technical goals, not necessarily application or product goals (yet). An important deliverable at this stage is the formal research requirements document (with well-specified verification and validation (V&V¹⁰) steps). A requirement is a singular documented physical or functional need that a particular design, product, or process aims to satisfy. Requirements aim to specify all stakeholders’ needs while not specifying a specific solution. Definitions are incomplete without corresponding measures for verification and validation (V&V). Verification: Are we building the product right? Validation: Are we building the right product?¹⁰ Here is one of several key decision points in the broader process: The R&D team considers several paths forward and sets the course: (A) prototype development towards Level 3, (B) continued R&D for longer-term research initiatives and/or publications, or some combination of A and B. We find the culmination of this stage is often a bifurcation: some work moves to applied ML, while some circles back for more research. This common MLTRL cycle is an instance of the non-monotonic discovery switchback mechanism (detailed in the “Methods” section and Fig. 3).
The difference is the former is circumstantial while the latter is predefined in the process. The other embedded switchback we define in the main MLTRL process is from level 9 to 4, shown in Fig. 4. While it is true that the majority of ML projects start at a reasonable readiness out of the box, e.g. level 4, this can make it challenging and problematic to switchback to R& D levels that the team have not encountered and may not be equipped for. In the right diagram we show a common review switchback from Level 5 to 4 (staying in the prototyping phase (orange)), and a switchback (faded) that should not be implemented because the prior level was not explicitly done; level 2 is squarely in the research pipeline (red).
Level 2 data—Datasets at this stage may include publicly available benchmark datasets, semi-simulated data based on the data sample in Level 1, or fully simulated data based on certain assumptions about the potential deployment environments. The data should allow researchers to characterize model properties, and highlight corner cases or boundary conditions, in order to justify the utility of continuing R&D on the model.
Level 2 review—To graduate from the PoP stage, the technology needs to satisfy research claims made in previous stages (brought to bear by the aforementioned PoP data in both quantitative and qualitative ways) with the analyses well-documented and reproducible.
Here we have checkpoints that push code development towards interoperability, reliability, maintainability, extensibility, and scalability. Code becomes prototype-caliber: A significant step up from research code in robustness and cleanliness. This needs to be well-designed, well-architected for dataflow and interfaces, generally covered by unit and integration tests, meet team style standards, and sufficiently documented. Note the programmers’ mentality remains that this code will someday be refactored/scrapped for productization; prototype code is relatively primitive with regard to the efficiency and reliability of the eventual system. With the transition to Level 4 and proof-of-concept mode, the working group should evolve to include product engineering to help define service-level agreements and objectives (SLAs and SLOs) of the eventual production system.
Level 3 data—For the most part consistent with Level 2; in general, the previous level review can elucidate potential gaps in data coverage and robustness to be addressed in the subsequent level. However, for test suites developed at this stage, it is useful to define dedicated subsets of the experiment data as default testing sources, as well as set up mock data for specific functionalities and scenarios to be tested.
Level 3 review—Teammates from applied AI and engineering are brought into the review to focus on sound software practices, interfaces and documentation for future development, and version control for models and datasets. There are likely domain- or organization-specific data management considerations going forward that this review should point out—e.g. standards for data tracking and compliance in healthcare¹¹.
This stage is the seed of application-driven development; for many organizations this is the first touch-point with product managers and stakeholders beyond the R&D group. Thus TRL Cards and requirements documentation are instrumental in communicating the project status and onboarding new people. The aim is to demonstrate the technology in a real scenario: quick proof-of-concept examples are developed to explore candidate application areas and communicate the quantitative and qualitative results. It is essential to use real and representative data for these potential applications. Thus data engineering for the PoC largely involves scaling up the data collection and processing from Level 1, which may include collecting new data or processing all available data using scaled experiment pipelines from Level 3. In some scenarios, there will be new datasets brought in for the PoC, for example, from an external research partner as a means of validation. Hand-in-hand with the evolution from sample to real data, the experiment metrics should evolve from ML research to the applied setting: proof-of-concept evaluations should quantify model and algorithm performance (e.g., precision and recall and various data splits), computational costs (e.g., CPU vs. GPU runtimes), and also metrics that are more relevant to the eventual end-user (e.g., number of false positives in the top-N predictions of a recommender system). We find this PoC exploration reveals specific differences between clean and controlled research data versus noisy and stochastic real-world data. The issues can be readily identified because of the well-defined distinctions between those development stages in MLTRL, and then targeted for further development.
AI ethics processes vary across organizations, but all should engage in ethics conversations at this stage, including ethics of data collection, and the potential of any harm or discriminatory impacts due to the model (as the AI capabilities and datasets are known). MLTRL requires ethics considerations to be reported on TRL Cards at all stages, which generally link to an extended ethics checklist. The key decision point here is to push onward with application development or not. It is common to pause projects that pass Level 4 review, waiting for a better time to dedicate resources, and/or pull the technology into a different project.
Level 4 data—Unlike the previous stages, having real-world and representative data is critical for the PoC; even with methods for verifying that data distributions in synthetic data reliably mirror those of real data, sufficient confidence in the technology must be achieved with real-world data of the use-case. Further, one must consider how to obtain high-quality and consistent data required for the future model inference: generation of the data pipeline PoC that will resemble the future inference pipeline that will take data from intended sources, transform it into features, and send it to the model for inference.
Level 4 review—Demonstrate the utility towards one or more practical applications (each with multiple datasets), taking care to communicate assumptions and limitations, and again reviewing data-readiness: evaluating the real-world data for quality, validity, and availability. The review also evaluates security and privacy considerations—defining these in the requirements document with risk quantification is a useful mechanism for mitigating potential issues (discussed further in the Methods section).
At this stage the technology is more than an isolated model or algorithm, it is a specific capability. For instance, producing depth images from stereo vision sensors on a mobile robot is a real-world capability beyond the isolated ML technique of self-supervised learning for RGB stereo disparity estimation. In many organizations, this represents a technology transition or handoff from R&D to productization. MLTRL makes this transition explicit, evolving the requisite work, guiding documentation, objectives and metrics, and team; indeed, without MLTRL it is common for this stage to be erroneously leaped completely, as shown in Fig. 4. An interdisciplinary working group is defined, as we start developing the technology in the context of a larger real-world process—i.e., transitioning the model or algorithm from an isolated solution to a module of a larger application. Just as the ML technology should no longer be owned entirely by ML experts, steps have been taken to share the technology with others in the organization via demos, example scripts, and/or an API; the knowledge and expertise cannot remain within the R&D team, let alone an individual ML developer. Graduation from Level 5 should be difficult, as it signifies the dedication of resources to push this ML technology through productization. This transition is a common challenge in deep-tech, sometimes referred to as “the valley of death” because project managers and decision-makers struggle to allocate resources and align technology roadmaps to effectively move to Levels 6, 7, and onward. MLTRL directly addresses this challenge by stepping through the technology transition or handoff explicitly.
In the left diagram (a subset of the Fig. 1 pipeline, same colors), the arrows show a common development pattern with MLTRL in the industry): projects go back to the ML toolbox to develop new features (dashed line), and frequent, incremental improvements are often a practice of jumping back a couple of levels to Level 7 (which is the main systems integrations stage). At Levels 7 and 8 we stress the need for tests that run use-case-specific critical scenarios and data-slices, which are highlighted by a proper risk-quantification matrix⁷⁸. Reviews at these Levels commonly catch gaps or oversight in the test and validation scenarios, resulting in frequent cycles back to Level 7 from 8. Cycling back to previous lower levels is not just a late-stage mechanism in MLTRL, but rather “switchbacks” occur throughout the process. Cycling back to Level 7 from 8 for more tests is an example of a review switchback, while the solid line from Level 9 to 7 is an embedded switchback where MLTRL defines certain conditions that require cycling back levels—see more in the “Methods” section and throughout the text. In the right diagram, we show the more common approach in the industry (without using our framework), which skips essential technology transition stages (gray)—ML Engineers push straight through to deployment, ignoring important productization and systems integration factors. This will be discussed in more detail in the “Methods” section.
Level 5 data—For the most part consistent with Level 4. However, considerations need to be taken for scaling of data pipelines: there will soon be more engineers accessing the existing data and adding more, and the data will be getting much more use, including automated testing in later levels. With this scaling can come challenges with data governance. The data pipelines likely do not mirror the structure of the teams or broader organization. This can result in data silos, duplications, unclear responsibilities, and missing control of data over its entire lifecycle. These challenges and several approaches to data governance (planning and control, organizational, and risk-based) are detailed in Janssen et al.⁸.
Level 5 review—The verification and validation (V&V) measures and steps defined in earlier R&D stages (namely Level 2) must all be completed by now, and the product-driven requirements (and corresponding V&V) are drafted at this stage. We thoroughly review them here and make sure there is stakeholder alignment (at the first possible step of productization, well ahead of deployment).
The main work here is significant software engineering to bring the code up to product-caliber: This code will be deployed to users and thus needs to follow precise specifications, have comprehensive test coverage, well-defined APIs, etc. The resulting ML modules should be robustified towards one or more target use-cases. If those target use-cases call for model explanations, the methods need to be built and validated alongside the ML model, and tested for their efficacy in faithfully interpreting the model’s decisions—crucially, this needs to be in the context of downstream tasks and the end-users, as there is often a gap between ML explainability that serves ML engineers rather than external stakeholders¹². Similarly, we need to develop the ML modules with known data challenges in mind, specifically to check the robustness of the model (and broader pipeline) to changes in the data distribution between development and deployment.
The deployment setting(s) should be addressed thoroughly in the product requirements document, as ML serving (or deploying) is an overloaded term that needs careful consideration. First, there are two main types: internal, as APIs for experiments and other usages mainly by data science and ML teams, and external, meaning an ML model that is embedded or consumed within a real application with real users. The serving constraints vary significantly when considering cloud deployment vs on-premise or hybrid, batch or streaming, open-source solution or containerized executable, etc. Even more, the data at deployment may be limited due to compliance, or we may only have access to encrypted data sources, some of which may only be accessible locally—these scenarios may call for advanced ML approaches such as federated learning¹³ and other privacy-oriented ML¹⁴. And depending on the application, an ML model may not be deployable without restrictions; this typically means being embedded in a rules engine workflow where the ML model acts like an advisor that discovers edge cases in rules. These deployment factors are hardly considered in model and algorithm development despite their significant influence on modeling and algorithmic choices; that said, hardware choices typically are considered early on, such as GPU versus edge devices. It is crucial to make these systems decisions at Level 6—not too early that serving scenarios and requirements are uncertain, and not too late that corresponding changes to model or application development risk deployment delays or failures. This marks a key decision for the project lifecycle, as this expensive ML deployment risk is common without MLTRL (see Fig. 4).
Level 6 data—Additional data should be collected and operationalized at this stage towards robustifying the ML models, algorithms, and surrounding components. These include adversarial examples to check local robustness¹⁵, semantically equivalent perturbations to check the consistency of the model with respect to domain assumptions^16,17, and collecting data from different sources and checking how well the trained model generalizes to them. These considerations are even more vital in the challenging deployment domains mentioned above with limited data access.
Level 6 review—Focus is on the code quality, the set of newly defined product requirements, system SLA and SLO requirements, data pipelines spec, and an AI ethics revisit now that we are closer to a real-world use-case. In particular, regulatory compliance is mandated for this gated review; the data privacy and security laws are changing rapidly, and missteps with compliance can make or break the project.
For integrating the technology into existing production systems, we recommend the working group has a balance of infrastructure engineers and applied AI engineers—this stage of development is vulnerable to latent model assumptions and failure modes, and as such cannot be safely developed solely by software engineers. Important tools for them to build together include:
Tests that run use-case-specific critical scenarios and data-slices—a proper risk-quantification table will highlight these.
A “golden dataset” should be defined to baseline the performance of each model and succession of models—see the computer vision app example in Fig. 5—for use in the continuous integration and deployment (CI/CD) tests.
Complicated logic such as this can mask ML model performance lags and failures, and also emphasized the need for R& D-to-product handoff described in MLTRL. Additional emphasis is placed on ML tests that consider the mix of real-world data with user annotations (b, right) and synthetic data generated by Unity AI’s Perception tool and structured domain randomization (b, left).
Metamorphic testing: A software engineering methodology for testing a specific set of relations between the outputs of multiple inputs. When integrating ML modules into larger systems, a codified list of metamorphic relations¹⁸ can provide valuable verification and validation measures and steps.
Data intervention tests that seek data bugs at various points in the pipelines, downstream to measure the potential effects of data processing and ML on consumers or users of that data, as well as upstream at data ingestion or creation. Rather than using model performance as a proxy for data quality, it is crucial to use intervention tests that instead catch data errors with mechanisms specific to data validation.
These tests in particular help mitigate underspecification in ML pipelines, a key obstacle to reliably training models that behave as expected in deployment¹⁹. On the note of reliability, it is important that quality assurance engineers (QA) play a key role here and through Level 9, overseeing data processes to ensure privacy and security, and covering audits for downstream accountability of AI methods.
Level 7 data—In addition to the data for test suites discussed above, this level calls for QA to prioritize data governance: how data is obtained, managed, used, and secured by the organization. This was earlier suggested in level 5 (in order to preempt related technical debt) and is essential here at the main junction for integration, which may create additional governance challenges in light of downstream effects and consumers.
Level 7 review—The review should focus on the data pipelines and test suites; a scorecard like the ML Testing Rubric²⁰ is useful. The group should also emphasize ethical considerations at this stage, as they may be more adequately addressed now (where there are many test suites put into place) rather than close to shipping later.
The technology is demonstrated to work in its final form and under expected conditions. There should be additional tests implemented at this stage covering deployment aspects, notably A/B tests, blue/green deployment tests, shadow testing, and canary testing, which enables proactive and gradual testing for changing ML methods and data. Ahead of deployment, the CI/CD system should be ready to regularly stress test the overall system and ML components. In practice, problems stemming from real-world data are impossible to anticipate and design for—an upstream data provider could change formats unexpectedly or a physical event could cause the customer behavior to change. Running models in shadow mode for a period of time would help stress test the infrastructure and evaluate how susceptible the ML model(s) will be to performance regressions caused by data. We observe that ML systems with data-oriented architectures are more readily tested in this manner, and better surface data quality issues, data drifts, and concept drifts—this is discussed later in the Beyond Software Engineering section. To close this stage, the key decision is go or no-go for deployment, and when.
Level 8 data—If not already in place, there absolutely needs to be mechanisms for automatically logging data distributions alongside model performance once deployed.
Level 8 review—A diligent walkthrough of every technical and product requirement, showing the corresponding validations, and the review panel is representative of the full slate of stakeholders.
In deploying AI and ML technologies, there is a significant need to monitor the current version and explicit considerations towards improving the next version. For instance, performance degradation can be hidden and critical, and feature improvements often bring unintended consequences and constraints. Thus at this level, the focus is on maintenance engineering—i.e., methods and pipelines for ML monitoring and updating. Monitoring for data quality, concept drift, and data drift is crucial; no AI system without thorough tests for these can reliably be deployed. By the same token, there must be automated evaluation and reporting—if actuals²¹ are available, continuous evaluation should be enabled, but in many cases, actuals come with a delay, so it is essential to record model outputs to allow for efficient evaluation after the fact. To these ends, the ML pipeline should be instrumented to log system metadata, model metadata, and data itself.
Monitoring for data quality issues and data drifts is crucial to catch deviations in model behavior, particularly those that are non-obvious in the model or product end-performance. Data logging is unique in the context of ML systems: data logs should capture statistical properties of input features and model predictions, and capture their anomalies. With monitoring for data, concept, and model drifts, the logs are to be sent to the relevant systems, applied, and research engineers. The latter is often non-trivial, as the model server is not ideal for model “observability” because it does not necessarily have the right data points to link the complex layers needed to analyze and debug models. To this end, MLTRL requires the drift tests to be implemented at stages well ahead of deployment, earlier than standard practice. Again we advocate for data-first architectures rather than the software industry-standard design by services (discussed later), which aids in surfacing and logging the relevant data types and slices when monitoring AI systems. For retraining and improving models, monitoring must be enabled to catch training-serving skew and let the team know when to retrain. Towards model improvements, adding or modifying features can often have unintended consequences, such as introducing latencies or even bias. To mitigate these risks, MLTRL has an embedded switchback here: any component or module changes to the deployed version must cycle back to Level 7 (integrations stage) or earlier—see Fig. 4. Additionally, for quality ML products, we stress a defined communication path for user feedback without roadblocks to R&D; we encourage real-world feedback all the way to research, providing valuable problem constraints and perspectives.
Level 9 data—Proper mechanisms for logging and inspecting data (alongside models) is critical for deploying reliable AI and ML—systems that learn on data have unique monitoring requirements (detailed above). In addition to the infrastructure and test suites covering data and environment shifts, it’s important for product managers and other owners to be on top of data policy shifts in domains such as finance and healthcare.
Level 9 review—The review at this stage is unique, as it also helps in lifecycle management: at a regular cadence that depends on the deployed system and domain of use, owners and other stakeholders are to revisit this review and recommend switchbacks if needed (discussed in the Methods section). This additional oversight at deployment is shown to help define regimented release cycles of updated versions, and provide another “eye” check for stale model performance or other system abnormalities.
Notice MLTRL is defined as stages or levels, yet much of the value in practice is realized in the transitions: MLTRL enables teams to move from one level to the next reliably and efficiently and provides a guide for how teams and objectives evolve with the progressing technology.
MLTRL is designed to apply to many real-world use-cases involving data and ML, from simple regression models used for predictive modeling energy demand or anomaly detection in data centers, to real-time modeling in rideshare applications and motion planning in warehouse robotics. For simple use-cases MLTRL may be overkill, and a subset may suffice—for instance, model cards as demonstrated by Google for basic image classification. Yet this is a fine line, as the same cards-only approach in the popular “Huggingface” codebases is too simplistic for the language models they represent, deployed in domains that carry significant consequences. MLTRL becomes more valuable with more complex, larger systems and environments, especially in risk-averse domains.
In this section, we illustrate MLTRL in several real use-cases from a diverse array of domains. In each use-case, we first outline the specific challenges faced in that domain, then move on to demonstrate how these challenges are addressed in the MLTRL framework—highlighting the specific levels that deal with each challenge. Moreover, for each use-case we provide a step-by-step, level-by-level walk-through of how MLTRL is applied, thus outlining in concrete, real-world settings how the MLTRL framework should be utilized.
While most ML projects begin with a specific task and/or dataset, there are many that originate in ML theory without any target application—i.e., projects starting MLTRL at level 0 or 1. These projects nicely demonstrate the utility of MLTRL built-in switchbacks, bifurcating paths, and iteration with domain experts. An example we discuss here is a novel approach to representing data in generative vision models from Naud and Lavin²², which was then developed into state-of-the-art unsupervised anomaly detection, and targeted for two human-machine visual inspection applications: First, industrial anomaly detection, notably in precision manufacturing, to identify potential errors for human-expert manual inspection. Second, using the model to improve the accuracy and efficiency of neuropathology, the microscopic examination of neurosurgical specimens for cancerous tissue. In these human-machine teaming use-cases there are specific challenges impeding practical, reliable use:
Hidden feedback loops can be common and problematic in real-world systems influencing their own training data: over time the behavior of users may evolve to select data inputs they prefer for the specific AI system, representing some skew from the training data. In this neuropathology case, selecting whole-slide images that are uniquely difficult for manual inspection, or even biased by that individual user. Similarly, we see underlying healthcare processes can act as hidden confounders, resulting in unreliable decision support tools²³.
Model availability can be limited in many deployment settings: for example, on-premises deployments (common in privacy-preserving domains like healthcare and banking), edge deployments (common in industrial use-cases such as manufacturing and agriculture), or from the infrastructure’s inability to scale to the volume of requests. This can severely limit the team’s ability to monitor, debug, and improve deployed models.
Uncertainty estimation is valuable in many AI scenarios, yet not straightforward to implement in practice. This is further complicated with multiple data sources and users, each injecting generally unknown amounts of noise and uncertainties. In medical applications it is of critical importance, to provide measures of confidence and sensitivity, and for AI researchers through end-users. In anomaly detection, various uncertainty measures can help calibrate the false-positive versus false-negative rates, which can be very domain specific.
Costs of edge cases can be significant, sometimes risking expensive machine downtime or medical failures. This is exacerbated in anomaly detection anomalies are by definition rare so they can be difficult to train for, especially for the anomalies that are completely unseen until they arise in the wild.
End-user trust can be difficult to achieve, often preventing the adoption of ML applications, particularly in the healthcare domain and other highly regulated industries.
These and additional ML challenges such as data privacy and interpretability can inhibit ML adoption in clinical practice and industrial settings but can be mitigated with MLTRL processes. We’ll describe how in the context of the ref. 22 example, which began at level 0 with theoretical ML work on manifold geometries, and at level 5 was directed towards specialized human-machine teaming applications utilizing the same ML method under-the-hood.
Levels 0–1—From open-ended exploration of data-representation properties in various Riemmanian manifold curvatures, we derived from first principles and empirically identified a property with hyperbolic manifolds: when used as a latent space for embedding data without labels, the geometry organizes the data by its implicit hierarchical structure. Unsupervised computer vision was identified in reviews as a promising direction for proof-of-principle work.
Level 2—One approach for validating the earlier theoretical developments was to generate synthetic data to isolate very specific features in data we would expect represented in the latent manifold. The results showed promise for anomaly detection—using the latent representation of data to automatically identify images that are out-of-the-ordinary (anomalous), and also using the manifold to inspect how they are semantically different. Further, starting with an implicitly probabilistic modeling approach implied uncertainty estimation could be a valuable feature downstream. This made the level 2 key decision point clear: proceed with applied ML development.
Levels 3–5—Proof-of-concept development and reviews demonstrated promise for several commercial applications relevant to the business, and also highlighted the need for several key features (defined as R&D and product requirements): interpretability (towards end-user trust), uncertainty quantification (to show confidence scores), and human-in-the-loop (for domain expertise). Without the MLTRL PoC steps and review processes, these features can often be delayed until beta testing or overlooked completely—for example, the failures of applying IBM Watson in medical applications²⁴. For this technology, the applications to develop towards are anomaly detection in histopathology and manufacturing, specifically inspecting whole-slide images of neural tissue, and detecting defects in metallic surfaces, respectively. From the systems perspective, we suggest quantifying the uncertainties of components and propagating them through the system, which can improve safety and trust. Probabilistic ML methods, rooted in Bayesian probability theory, provide a principled approach to representing and manipulating uncertainty about models and predictions²⁵. For this reason, we advocate strongly for probabilistic models and algorithms in AI systems. In this machine vision example, the MLTRL technical requirements specifically called for a probabilistic generative model to readily quantify various types of uncertainties and propagate them forward to the visualization component of the pipeline, and the product requirements called for the downstream confidence and sensitivity measures to be exposed to the end-user. Component uncertainties must be assembled in a principled way to yield a meaningful measure of overall system uncertainty, based on which safe decisions can be made²⁶. See the “Methods” section for more on uncertainty in AI systems. The early checks for data management and governance proved valuable here, as the application areas dealt with highly sensitive data that would significantly influence the design of data pipelines and test suites. In both the neuropathology and manufacturing applications, the data management checks also raised concerns about hidden feedback loops, where users may unintentionally skew the data inputs when using the anomaly detection models in practice, for instance biasing the data towards specific subsets they subjectively need help with. Incorporating domain experts this early in the project lifecycle helped inform verification and validation steps to help be robust to the hidden feedback loops. Not to mention their input guided us towards user-centric metrics for performance, which can often skew from ML metrics in important ways—for instance, the typical acceptance ratio for false positives versus false negatives does not apply to select edge cases, for which our hierarchical anomaly classification scheme was useful²². From prior reviews and TRL card documentation, we also identified the value of synthetic data generation in application development: anomalies are by definition rare so they are hard to come by in real datasets, especially with evolving environments in deployment settings, so the ability to generate synthetic datasets for anomaly detection can accelerate the levels 6–9 pipeline, and help ensure more reliable models in the wild.
Level 6 (medical)—The medical inspection application experienced a bifurcation with product work proceeding while additional R&D was desired to explore improved data processing methods while engaging with clinicians and medical researchers for feedback. Proceeding through the levels in a non-linear, non-monotonic way is common in MLTRL and encouraged by various switchback mechanisms (detailed in the “Methods” section). These practices—intentional switchbacks, frequent engagement with domain experts and users—can help mitigate methodological flaws and underlying biases that are common when applying ML to clinical applications. For instance, recent work by Roberts et al.²⁷ investigated 2122 studies applying ML to COVID-19 use-cases, finding that none of the models are sufficient for clinical use due to methodological flaws and/or underlying biases. They go on to give many recommendations—some we’ve discussed in the context of MLTRL, and more—which should be reviewed for higher quality medical-ML models and documentation.
Level 6–9 (manufacturing)—Overall these stages proceeded regularly and efficiently for the defect detection product. MLTRL’s embedded switchback from level 9 to 4 proved particularly useful in this lifecycle, both for incorporating feedback from the field and for updating with research progress. On the former, the data distribution shifts from one deployment setting to another significantly affected false-positive versus false-negative calibrations, so this was added as a feature to the CI/CD pipelines. On the latter, the built-in touch points for real-world feedback and data into the continued ML research provided valuable constraints to help guide research, and product managers could readily understand what capabilities could be available for product integration and when (readily communicated with TRL Cards)—for instance, later adding support for video-based inspection for defects, and tooling for end-users to reason about uncertainty estimates (which helps establish trust).
Level 7–9 (medical)—For productization the “neuropathology copilot” was handed off to a partner pharmaceutical company to integrate into their existing software systems. The MLTRL documentation and communication streamlined the technology transfer, which can often be a time-consuming manual process. If not pursuing this path, the product would’ve likely faced many of the medical-ML deployment challenges with model availability and data access; MLTRL cannot overcome the technical challenges of deploying on-premises, but the manifestation of those challenges as performance regressions, data shifts, privacy and ethics concerns, etc. can be mitigated by the system-level checks and strategies MLTRL puts forth.
Advancements in physics engines and graphics processing have advanced AI environment and data-generation capabilities, putting increased emphasis on transitioning models across the simulation-to-reality gap^28,29,30. To develop a computer vision application for automated recycling, we leveraged the Unity Perception³¹ package, a toolkit for generating large-scale datasets for perception-based ML training and validation. We produced synthetic images to complement real-world data sources (Fig. 5). This application exemplifies three important challenges in ML product development that MLTRL helps overcome:
Multiple and disparate data sources are common in deployed ML pipelines yet often ignored in R&D. For instance, upstream data providers can change formats unexpectedly, or a physical event could cause the customer behavior to change. It is nearly impossible to anticipate and design for all potential problems with real-world data and deployment. This computer vision system implemented pipelines and extended test suites to cover open-source benchmark data, real user data, and synthetic data.
Hidden performance degradation can be challenging to detect and debug in ML systems because gradual changes in performance may not be immediately visible. Common reasons for this challenge are that the ML component may be one step in a series. Additionally, local/isolated changes to an ML component’s performance may not directly affect the observed downstream performance. We can see both issues in the illustrated logic diagram for the automated recycling app (Fig. 5). A slight degradation in the initial CV model may not heavily influence the following user input. However, when an uncommon input image appears in the future, the app fails altogether.
Model usage requirements can make or break an ML product. For example, the Netflix “$1M Prize” solution was never fully deployed because of significant engineering costs in real-world scenarios (netflixtechblog.com/netflix-recommendations-beyond-the-5-stars-part-1). For example, engineering teams must communicate memory usage, compute power requirements, hardware availability, network privacy, and latency to the ML teams. ML teams often only understand the statistics or ML theory behind a model but not the system requirements or how it scales.
We next elucidate these challenges and how MLTRL helps overcome them in the context of this project’s lifecycle. This project started at level 4, using largely existing ML methods with a target use case. Specifically, the computer vision (CV) model for object recognition and classification was off-the-shelf, allowing us to bypass levels 0 and 1. Similarly, the synthetic data generation method used Unity Perception, a well-established open-source project. Additionally, the previous project established model training and data pipelines for production, allowing us to bypass level 3.
Level 4—Though previous work allowed us to skip the earlier levels, many challenges arise when combining ML elements that were independently validated and developed. The MLTRL prototype-caliber code checkpoint ensures that the existing code components are validated and helps avoid poorly defined borders and abstractions between components. ML pipelines often grow out of glue code, and our regimented code checkpoints motivate well-architected software that minimizes these danger spots.
Level 5—The problematic “valley of death”, mentioned earlier in the level 5 definitions, is less prevalent in use-cases like this that start at a higher MLTRL level with a specific product deliverable. In this case, the product deliverable was a real-time object recognition and classification of trash for a mobile recycling application. Still, this stage is critical for the requirements and V&V transition. This stage mitigates failure risks due to the disparate data sources integrated at various steps in this CV system and accounted for the end-user compute constraints for mobile computing. Specifically, the TRL cards from earlier stages surfaced potential issues with imbalanced datasets and the need for specific synthetic images. These considerations are essential for the data readiness and testing of V&V in the productization requirements. Data quality and availability issues often present huge blockers because teams discover them too late in the game. Data-readiness is one class of many example issues teams face without MLTRL, as depicted in Fig. 4.
Level 6—We were re-using a well-understood model and deployment pipeline in this use-case, meaning our primary challenge was around data reliability. For the problem of recognizing and classifying trash, building a reliable data source using only real data is almost impossible due to diversity, class imbalance, and annotation challenges. Therefore we chose to develop a synthetic data generator to create training data. At this MLTRL level, we needed to ensure that the synthetic data generator created sufficiently diverse data and exposed the controls needed to alter the data distribution in production. Therefore, we carefully exposed APIs using the Unity Perception package, which allowed us to control lighting, camera parameters, target and non-target object placements and counts, and background textures. Additionally, we ensured that the object labeling matched the real-world annotator instructions and that output data formats matched real-world counterparts. Lastly, we established a set of statistical tests to compare synthetic and real-world data distributions. The MLTRL checks ensured that we understood, and in this case, adequately designed our data sources to meet in-production requirements.
Level 7—From the previous level’s R&D TRL cards and observations, we knew relatively early in productization that we would need to assume bias for the real data sources due to class imbalance and imperfect annotations. Therefore we designed tests to monitor these in the deployed application. MLTRL imposes these critical deployment tests well ahead of deployment, where we can easily overlook ML-specific failure modes.
Level 8—As we suggested earlier, problems that stem from real-world data are near impossible to anticipate and design for, implying the need for level 8 mission-readiness preparations. Given that we were generating synthetic images (with structured domain randomization) to complement the real data, we created tests for different data distribution shifts at multiple points in the classification pipeline. We also implemented thorough shadow tests ahead of deployment to evaluate how susceptible the ML model(s) to performance regressions caused by data. Additionally, we also implemented these as CI/CD tests over various deployment scenarios (or mobile device computing specifications). Without these fully covered, documented, and automated, it would be impossible to pass the Level 8 review and deploy the technology.
Level 9—Post-deployment, the monitoring tests prescribed in Levels 8 and 9, and the three main code quality checkpoints in the MLTRL process help surface hidden performance degradation problems, common with complex pipelines of data flows and various models. The switchbacks depicted in Fig. 4 are typical in CV use-cases. For instance, miscalibrations in models pre-trained on synthetic data and fine-tuned on newer real data can be common yet difficult to catch. However, the level 7 to 4 switchback is designed precisely for these challenges and product improvements.
Computational models and simulation are key to scientific advances at all scales, from particle physics to material design and drug discovery, to weather and climate science, and to cosmology³². Many simulators model the forward evolution of a system (coinciding with the arrow of time), such as the interaction of elementary particles, diffusion of gasses, folding of proteins, or evolution of the universe on the largest scale. The task of inference refers to finding initial conditions or global parameters of such systems that can lead to some observed data representing the final outcome of a simulation. In probabilistic programming³³, this inference task is performed by defining prior distributions over any latent quantities of interest, and obtaining posterior distributions over these latent quantities conditioned on observed outcomes (for example, experimental data) using the Bayes rule. This process, in effect, corresponds to inverting the simulator such that we go from the outcomes toward the inputs that caused the outcomes. In the “Etalumis” project³⁴ (“simulate” spelled backward), we are using probabilistic programming methods to invert existing, large-scale simulators via Bayesian inference. The project is an interdisciplinary collaboration of specialists in probabilistic machine learning, particle physics, and high-performance computing, all essential elements to achieve the project outcomes. Even more, it is a multi-year project spanning multiple countries, companies, university labs, and government research organizations, bringing significant challenges in project management, technology coordination, and validation. Aided by MLTRL, there were several key challenges to overcome in this project that is common in scientific-ML projects:
Integrating with legacy systems is common in scientific and industrial use-cases, where ML methods are applied with existing sensor networks, infrastructure, and codebases. In this case, particle physics domain experts at CERN are using the SHERPA simulator³⁵, a 1 million-line codebase developed over the last two decades. Rewriting the simulator for ML use-cases is infeasible due to the codebase size and buried domain knowledge, and new ML experts would need significant onboarding to gain a working knowledge of the codebase. It is also common to work with legacy data infrastructure, which can be poorly organized for machine learning (let alone preprocessed and clean) and unlikely to have followed best practices such as dataset versioning.
Coupling hardware and software architectures are non-trivial when deploying ML at scale, as performance constraints are often considered in deployment tests well after model and algorithm development, not to mention the expertise is often split across disjoint teams. This can be exacerbated in scientific-ML when scaling to supercomputing infrastructure, and working with massive datasets that can be in the terabytes and petabytes.
Interpretability is often a desired feature yet difficult to deliver and validate in practice. Particularly in scientific ML applications such as this, mechanisms and tooling for domain experts to interpret predictions and models are key for usability (integrating into workflows and building trust).
To this end, we will go through the MLTRL levels one by one, demonstrating how they ensure the above scientific ML challenges are diligently addressed.
Level 0—The theoretical developments leading to Etalumis are immense and well discussed in ref. 34. In particular, the ML theory and methods are in a relatively nascent area of ML and mathematics, probabilistic programming. New territory can present more challenges compared to well-traveled research paths, for instance in computer vision with neural networks. It is thus helpful to have a guiding framework when making a new path in ML research, such as MLTRL where early reviews help theoretical ML projects get legs.
Level 1–2—Running low-level experiments in simple testbeds is generally straightforward when working with probabilistic programming and simulation; in a sense, this easy iteration over experiments is what PPL is designed for. It was additionally helpful in this project to have rich data grounded in physical constraints, allowing us to better isolate model behaviors (rather than data assumptions and noise). The MLTRL requirements documentation is particularly useful for the standard PPL experimentation workflow: model, infer, criticize, repeat (or Box’s loop)³⁶. The evaluation step (i.e. criticizing the model) can be more nuanced than checking summary statistics as in deep learning and similar ML workflows. It is thus a useful practice to write down the criticism methods, metrics, and expected results as verifications for specific research requirements, rather than iterating over Box’s loop without a priori targets. Further, because this research project had a specific target application early in the process (the SHERPA simulator), the project timeline benefited from recognizing simulator-integration constraints upfront as requirements, not to mention data availability concerns, which are often overlooked in early R&D levels. It was additionally useful to have CERN scientists as domain experts in the reviews at these R&D levels.
Level 3—Systems development can be challenging with probabilistic programming, again because it is relatively nascent and much of the out-of-the-box tools and infrastructure are not there as in most ML and deep learning. Here in particular there’s a novel (unproven) approach for systems integration: a probabilistic programming execution protocol was developed to reroute random number draws in the stochastic simulator codebase (SHERPA) to the probabilistic programming system, thus enabling the system to control stochastic choices in SHERPA and run inference on its execution traces, all while keeping the legacy codebase intact! A more invasive method that modifies SHERPA would not have been acceptable. If it were not for MLTRL forcing systems considerations this early in the Etalumis project lifecycle, this could have been an insurmountable hurdle later when multiple codebases and infrastructures come into play. By the same token, systems planning here helped enable the significant HPC scaling later: the team defined the need for HPC support well ahead of actually running HPC, in order to build the prototype code in a way that would readily map to HPC (in addition to local or cloud CPU and GPU). The data engineering challenges in this system’s development nonetheless persist—that is, data pipelines and APIs that can integrate various sources and infrastructures, and normalize data from various databases—although MLTRL helps consider these at an earlier stage that can help inform architecture design.
Level 4—The natural “embedded switchback” from Level 4 to 2 (see the “Methods” section) provided an efficient path toward developing an improved, amortized inference method–i.e., using a computationally expensive deep learning-based inference algorithm to train only once, in order to then do fast, repeated inference in the SHERPA model. Leveraging cyclic R&D methods, the Etalumis project could iteratively improve inference methods without stalling the broader system development, ultimately producing the largest scale posterior inference in a Turing-complete probabilistic programming system. Achieving this scale through iterative R&D along the main project lifecycle was additionally enabled by working with National Energy Research Scientific Computing (NERSC) engineers and their Cori supercomputer to progressively scale smaller R&D tests to the goal supercomputing deployment scenario. Typical ML workflows that follow simple linear progressions^37,38 would not enable ramping up in this fashion, and can actually prevent scaling R&D to production due to a lack of systems engineering processes (like MLTRL) connecting research to deployment.
Level 5—Multi-org international collaborations can be riddled with communication and teamwork issues, in particular at this pivotal stage where teams transition from R&D to application and product development. First, MLTRL as a lingua franca was key to the team effort in bringing Etalumis proof-of-concept into the larger effort of applying it to massive high-energy physics simulators. It was also critical at this stage to clearly communicate end-user requirements across the various teams and organizations, which must be defined in MLTRL requirements docs with V&V measures—the essential science-user requirements were mainly for model and prediction interpretability, uncertainty estimation, and code usability. If there are concerns over these features, MLTRL switchbacks can help to quickly cycle back and improve modeling choices in a transparent, efficient way— generally, in ML projects, these fundamental issues with usability are caught too late, even after deployment. In the probabilistic generative model setting, we’ve defined in Etalumis, Bayesian inference gives results that are interpretable because they include exact locations and processes in the model that are associated with each prediction. Working with ML methods that are inherently interpretable, we are well-positioned to deliver interpretable interfaces for the end-users later in the project lifecycle.
Level 6–9—The standard MLTRL protocol applies in these application-to-deployment stages, with several Etalumis-specific highlights. First, given the significant research contributions in both probabilistic programming and scientific ML, it’s important to share the code publicly. The development and deployment of the open-source code repository PPX (github.com/pyprob/ppx) branched into a separate MLTRL path from the Etalumis path for deployment at CERN. It’s useful to have systems engineering enable a clean separation of requirements, deployments, etc. when there are different development and product lifecycles originating from a common parent project. For example, in this case, it was useful to employ MLTRL switchbacks in the open-sourcing process, isolated from the CERN application paths, in order to add support for additional programming languages so PPX can apply to more scientific simulators—both directions benefited significantly from the data pipelines considerations brought up levels earlier, where open-sourcing required different data APIs and data transformations to enable broad usability. Second, related to the open-source code deliverable and the scientific ML user requirements we noted above, the late stages of MLTRL reviews include higher-level stakeholders and specific end-users, yet again enforcing these scientific usability requirements are met. An example result of this in Etalumis is the ability to output human-readable execution traces of the SHERPA runs and inference, enabling never-before-possible step-by-step interpretability of the black-box simulator.
The scientific ML perspective additionally brings to the forefront an end-to-end data perspective that is pertinent in essentially all ML use-cases: these systems are only useful to the extent they provide comprehensive data analyses that integrate the data consumed and generated in these workflows, from raw domain data to machine-learned models. These data analyses drive reproducibility, explainability, and experiment data understanding, which are critical requirements in scientific endeavors and ML broadly.
Understanding cause and effect relationships are crucial for accurate and actionable decision-making in many settings, from healthcare and epidemiology to economics and government policy development. Unfortunately, standard machine learning algorithms can only find patterns and correlations in data, and as correlation is not causation, their predictions cannot be confidently used for understanding cause and effect. Indeed, relying on correlations extracted from observational data to guide decision-making can lead to embarrassing, costly, and even dangerous mistakes, such as concluding that asthma reduces pneumonia mortality risk³⁹ and that smoking reduces the risk of developing severe COVID-19⁴⁰. Fortunately, there has been much recent development in a field known as a causal inference that can quantitatively make sense of cause and effect from purely observational data⁴¹. The ability of causal inference algorithms to quantify causal impact rests on a number of important checks and assumptions—beyond those employed in standard machine learning or purely statistical methodology—that must be carefully deliberated over during their development and training. These specific checks and assumptions are as follows:
Specifying cause-and-effect relationships between relevant variables: One of the most important assumptions underlying causal inference is the structure of the causal relations between quantities of interest. The gold standard for determining causal relations is to perform a randomized controlled trial, but in most cases, these cannot be employed due to ethical concerns, technological infeasibility, or prohibitive cost. In these situations, domain experts have to be consulted to determine the causal relationships. It is important in these situations to carefully address the manner in which such domain knowledge was extracted from experts, the number and diversity of experts involved, the amount of consensus between experts, and so on. The need for careful documentation of this knowledge and its periodic review is made clear in the MLTRL framework, as we shall see below.
Identifiability: Another vital component of building causal models is whether the causal question of interest is identifiable from the causal structure specified for the model together with observational (and sometimes experimental) data.
Adjusting for and monitoring confounding bias: An important aspect of causal model performance, not present in standard machine learning algorithms, is confounding bias adjustment. The standard approach is to employ propensity score matching to remove such bias. However, the quality of bias adjustment achieved in any specific instance with such propensity-based matching methods needs to be checked and documented, with alternate bias adjusting procedures required if appropriate levels of bias adjustment are not achieved⁴².
Sensitivity analysis: As causal estimates are based on generally untestable assumptions, such as observing all relevant confounders, it is vital to determine how sensitive the resulting predictions are to potential violations of these assumptions.
Consistency: It is crucial to understand if the learned causal estimate provably converges to the true causal effect within the limit of an infinite sample size. However, causal models cannot be validated by standard held-out tests, but rather require randomization or special data collection strategies to evaluate their predictions^43,44.
The MLTRL framework makes transparent the need to carefully document and defends these assumptions, thus ensuring the safe and robust creation, deployment, and maintenance of causal models. We elucidate this with recent work by Richens et al.⁴⁵, developing a causal approach to computer-assisted diagnosis which outperforms previous purely machine learning-based methods. To this end, we will go through the MLTRL levels one by one, demonstrating how they ensure the above specific checks and assumptions are naturally accounted for. This should provide a blueprint for how to employ the MLTRL levels in other causal inference applications.
Level 0—When initially faced with a causal inference task, the first step is always to understand the causal relationships between relevant variables. For instance, in ref. 45, the first step toward building the diagnostic model was specifying the causal relationships between the diverse set of risk factors, diseases, and symptoms included in the model. To learn these relations, doctors and healthcare professionals were consulted to employ their expansive medical domain knowledge which was robustly evaluated by additional independent groups of healthcare professionals. The MLTRL framework ensured this issue is dealt with and documented correctly, as such knowledge is required to progress from Level 0; failure to do this has plagued similar healthcare AI projects⁴⁶. The next step of any causal analysis is to understand whether the causal question of interest is uniquely identifiable from the causal structure specified for the model together with observational and experimental data. In this medical diagnosis example, identification was crucial to establish, as the causal question of interest, “would the observed symptoms not be present had a specific disease been cured?”, was highly non-trivial. Again, MLTRL ensures this vital aspect of model building is carefully considered, as a mathematical proof of identifiability would be required to graduate from Level 0. With both the causal structure and identifiability result in hand, one can progress to Level 1.
Level 1—At this level, the goal is to take the estimand for the identified causal question of interest and devise a way to estimate it from data. To do this one will need efficient ways to adjust for confounding bias. The standard approach is to employ propensity score-based methods to remove such bias when the target decision is binary, and use multi-stage ML models adhering to the assumed causal structure⁴⁷ for continuous target decisions (and high-dimensional data in general). However, the quality of bias adjustment achieved in any specific instance with propensity-based matching methods needs to be checked and documented, with alternate bias adjusting procedures required if appropriate levels of bias adjustment are not achieved⁴². As above, MLTRL ensures transparency and adherence to this important aspect of causal model development, as without it a project cannot graduate from Level 1. Even more, MLTRL ensures tests for confounding bias are developed early on and maintained throughout later stages of deployment. Still, in many cases, it is not possible to completely remove confounding in the observed data. TRL Cards offer a transparent way to declare specific limitations of a causal ML method.
Level 2—PoC-level tests for causal models must go beyond that of typical ML models. As discussed above, to ensure the estimated causal effects are robust to the assumptions required for their derivation, sensitivity to these assumptions must be analyzed. Such sensitivity analysis is often limited to R&D experiments or a post-hoc feature of ML products. MLTRL on the other hand requires this throughout the lifecycle as components of ML test suites and gated reviews. In the case of causal ML, best practice is to employ sensitivity analysis for this robustness check⁴⁸. MLTRL ensures this check is highlighted and adhered to, and no model will end up graduating Level 2–let alone being deployed—unless it is passed.
Level 3—Coding best practices, as in general ML applications.
Level 4–5—There are additional tests to consider when taking causal models from research to production, in particular at Level 4—proof of concept demonstration in a real scenario. Consistency, for example, is an important property of causal methods that informs us whether the method provably converges to the true causal graph within the limit of infinite sample size. Quantifying consistency in the test suite is critical when datasets change from controlled laboratory settings to open-world, and when the application scales. And PoC validation steps are more efficient with MLTRL because the process facilitates early specification of the evaluation metric for a causal model in Level 2. Causal models cannot be validated by standard held-out tests, but rather require randomization or special data collection strategies to evaluate their predictions^43,44. Any difficulty in evaluating the model’s predictions will be caught early and remedied.
Level 6–9—With the causal ML components of this technology developed reliably in the previous levels, the rest of the levels developing this technology focused on general medical-ML deployment challenges. For the most part, data governance, privacy, and management that was detailed earlier in the neuropathology MLTRL use-case, as well as the on-premises deployment.
The Cameras for Allsky Meteor Surveillance (CAMS) project⁴⁹, established in 2010 by NASA, uses hundreds of off-the-shelf CCTV cameras to capture meteor activity in the night sky. Initially, resident scientists would retrieve hard disks containing video data captured each night and perform manual triangulation of tracks or streaks of light in the night sky, and compute a meteor’s trajectory, orbit, and light curve. Each solution was manually classified as a meteor or not (i.e., planes, birds, clouds, etc.). In 2017, a project run by the Frontier Development Lab⁵⁰ (The NASA Frontier Development Lab and partners open-source the code and data via the SpaceML platform: spaceml.org), the AI accelerator for NASA and ESA, aimed to automate the data processing pipeline and replicate the scientists thought process to build an ML model that identifies meteors in the CAMS project^51,52. The data automation led to orders of magnitude improvements in the operational efficiency of the system and allowed new contributors and amateur astronomers to start contributing to meteor sightings. Additionally, a novel web tool allowed anybody anywhere to view the meteors detected the previous night. The CAMS camera system has had a six-fold global expansion of the data capture network, discovered ten new meteor showers, contributed towards instrumental evidence of previously predicted comets, and helped calculate parent bodies of various meteor showers. CAMS utilized the MLTRL framework to progress as described:
Level 1—Understanding the domain and data is a prerequisite for any ML development. Extensive data exploration elucidated visual differences between objects in the night sky such as meteors, satellites, clouds, tail lights of planes, and light from the eyes of cats peering into cameras, trees, and other tall objects visible in the moonlight. This step helped (1) understand the visual properties of meteors that later defined the ML model architecture, and (2) mitigate the impact of data imbalance by proactively developing domain-oriented strategies. The results are well-documented on a datasheet associated with the TRL card and discussed at the stage review. This MLTRL documentation forced us to consider data sharing and other privacy concerns at this early conceptualization stage, which is certainly relevant considering CAMS is for open-source and gathering data from myriad sources.
Level 2–3—The agile and non-monotonic (or non-linear) development prescribed by MLTRL allowed the team to first develop an approximate end-to-end pipeline that offered a path to ML model deployment and quick turnaround time to incorporate feedback from the regular gated reviews. Then, with relatively quicker experimentation, the team could improve on the quality of not just the ML model, but also scale up the systems development simultaneously in a non-monotonic development cycle.
Level 4—With the initial pipeline in place, scalable training of baselines and initial models on real challenging datasets ensued. Throughout the levels, the MLTRL gated reviews were essential for making efficient progress while ensuring robustness and functionality that meets stakeholder needs. At this stage we highlight specific advantages of the MLTRL review processes that had an instrumental effect on the project’s success: With the required panel of mixed ML researchers and engineers, domain scientists, and product managers, the stage 4 reviews stressed the significance of numerical improvements and comparison to existing baselines and helped identify and overcome issues with data imbalance. The team likely would have overlooked these approaches without the review from peers in diverse roles and teams. In general, the evolving panel of reviewers at different stages of the project was essential for covering a variety of verification and validation measures—from helping mitigate data challenges to open-source code quality.
Level 5—To complete this R&D-to-productization level, a novel web tool called the NASA CAMS Meteor Shower Portal (meteorshowers.seti.org) was created that allowed users to view meteor shower activity from the previous night and verify meteor predictions generated by the ML model. This app development was valuable for A/B testing, validating detected meteors and classifying new meteor showers with human–AI interaction, and demonstrating real-world utility to stakeholders in review. ML processes without MLTRL miss out on this valuable development by overlooking the need for such a demo tool.
Level 6—Application development was naturally driven by end-user feedback from the web app in level 5—without MLTRL it’s unlikely the team would be able to work with early productization feedback. With almost real-time feedback coming in daily, newer methods for improving the robustness of meteor identification led to researching and developing a unique augmentation technique, resulting in the state-of-the-art performance of the ML model. Further application development led to incorporating features that were in demand by users of the NASA CAMS Meteor Shower Portal: including celestial reference points through constellations, adding the ability to zoom in/out and (un)cluster showers, and providing tooling for scientific communication. The coordination of these features into a product-caliber codebase resulted in the release of the NASA CAMS Meteor Shower Portal 2.0 which was built by a team of citizen scientists—again we found the specific checkpoints in the MLTRL review were crucial for achieving these goals.
Level 7—Integration was particularly challenging in two ways. First, integrating the ML and data engineering deliverables with the existing infrastructure and tools of the larger CAMS system, which had started development years earlier with other teams in partner organizations, required quantifiable progress for verifying the tech-readiness of ML models and modules. The use of technology readiness levels provided a clear and consistent metric for the maturity of the ML and data technologies, making for clear communication and efficient project integration. Without MLTRL it is difficult to have a conversation, let alone make progress, towards integrating ML/AI and data subsystems and components. Second, integrating open-source contributions into the main ML subsystem was a significant challenge alleviated with diligent verification and validation measures from MLTRL, as well as quantifying robustness with ML testing suites (using scoring measures like that of the ML Testing Rubric²⁰, and devising a checklist based on metamorphic testing¹⁸).
Level 8—CAMS, like many datasets in practice, consists of a smaller labeled subset and a much larger unlabeled set. In an attempt to additionally increase the robustness of the ML subsystem ahead of “mission-readiness”, we looked to active learning^53,54 techniques to leverage the unlabeled data. Models using an initial version of this approach, where results of the active learning provided “weak” labels, resulted in consumption of the entire decade-long unlabeled data collected by CAMS and slightly higher scores on deployment tests. Active learning showed to be a promising feature and was switched back to level 7 for further development towards the next deployment version, so as not to delay the rest of the project.
Level 9—The ML components in CAMS require continual monitoring for model and data drifts, such as changes in weather, smoke, and cloud patterns that affect the view of the night sky. The data drifts may also be specific to locations, such as fireflies and bugs in CAMS Australia and New Zealand stations which appear as false positives. The ML pipeline is largely automated with CI/CD, runs regular regression tests, and production of benchmarks. Manual intervention can be triggered when needed, such as sending low-confidence meteors for verification to scientists in the CAMS project. The team also regularly releases the code, models, and web tools on the open-source space sciences and exploration ML toolbox, SpaceML (spaceml.org). Through the SpaceML community and partner organizations, CAMS continually improves with feature requests, debugging, and improving data practices, while tracking progress with standard software release cycles and MLTRL documentation.
Software engineering (SWE) practices vary significantly across domains and industries. Some domains, such as medical applications, aerospace, or autonomous vehicles rely on a highly rigorous development process which is required by regulations. Other domains, for example, advertising and e-commerce are not regulated and can employ a lenient approach to development. ML development should at minimum inherit the acceptable software engineering practices of the domain. There are, however, several key areas where ML development stands out from SWE, adding its own unique challenges which even the most rigorous SWE practices are not able to overcome.
For instance, the behavior of ML systems is learned from data, not specified directly in code. The data requirements around ML (i.e., data discovery, management, and monitoring) add significant complexity not seen in other types of SWE. There are many benefits to using a data-oriented architecture (DOA)⁴⁶ with the data-first workflows and management practices prescribed in MLTRL. DOA aims to make the data flowing between elements of business logic more explicit and accessible with a streaming-based architecture rather than the micro-service architectures that are standard in software systems. One specific benefit of DOA is making data available and traceable by design, which helps significantly in the ML logging challenges and data governance needs we discussed in Levels 7–9. Moreover, MLTRL highlights data-related requirements along every step to ensure that the development process considers data readiness and availability.
There are an array of ML-specific failure modes that must be carefully addressed before ML algorithms are deployed. For example, models become miscalibrated due to subtle data distributional shifts in the deployment setting, resulting in models that are more confident in predictions than they should be. MLTRL helps define ML-specific testing considerations (levels 5 and 7) to help surface ML-specific failure modes early. ML opens up new threat vectors across the whole deployment workflow that otherwise are not risks in software systems: for example, a poisoning attack to contaminate the training phase of ML systems, or membership inference to see if a given data record was part of the model’s training. MLTRL considers these threat vectors and suggests relevant risk identification during the prototyping and productization phases. More generally, ML codebases have all the problems for regular code, plus ML-specific issues at the system level, mainly as a consequence of added complexity and dynamism. The resulting entanglement, for instance, implies that the SWE practice of making isolated changes is often not feasible—Scully et al.⁵⁵ refer to this as the “changing anything changes everything” principle. Given this consideration, typical SWE change-management is insufficient. Furthermore, ML systems almost necessarily increase the technical debt; package-level refactoring is generally sufficient for removing technical debt in software systems, but this is not the case in ML systems.
These factors and others suggest that inherited software engineering and management practices of a given domain are insufficient for the successful development of robust and reliable ML systems. But it is not trading off one for the other: MLTRL can be used in synergy with the existing, industry-standard software engineering practices such as agile⁵⁶ and waterfall⁵⁷ to handle unique challenges of ML development. Because ML applications are a category of software, all best practices of building and operating software should be extended when possible to the ML application. Practices like version control, comprehensive testing, continuous integration, and continuous deployment are all applicable to ML development. MLTRL provides a framework that helps extend SWE building and operating practices that are acceptable in a given domain to tackle the unique challenges of ML development.
A recent case study from Microsoft Research³⁸ similarly identifies a few themes describing how ML is not equal to software engineering, and recommends a linear ML workflow with steps for data preparation through modeling and deploying. They define an effective workflow for isolated development of an ML model, but this approach does not ensure the technology is actually improving in quality and robustness. Their process should be repeated at progressive stages of development in the broader ML and data technology lifecycle. If applied in the MLTRL framework, the specific ingredients of the ML model workflow—that is, people, software, tests, objectives, etc. —evolve over time and subsequent stages as the technologies mature.
There exist many recommended workflows for specific ML methods and areas of pipelines. For instance, a more iterative process for Bayesian ML⁵⁸ and even more specifically for probabilistic programming³⁶, a data mining process defined in 2000 that remains widely used⁵⁹, others for describing data iterations⁶⁰, and human–computer interaction cycles⁶¹. In these recommended workflows and others, there is an important distinction between their cycles and “switchback” mechanisms in MLTRL. Their cycles suggest generically iterating over a data-modeling–evaluation–deployment process. Switchbacks, on the other hand, are specific, purpose-driven workflows for dialing part(s) of a project to an earlier stage—this does not simply mean going back and training the model on more data, but rather switching back regresses the technology’s maturity level (e.g. from level 5 to level 3) such that it must again fulfill the level-by-level requirements, evaluations, and reviews. See the “Methods” section for more details on MLTRL switchbacks. In general, iteration is an important part of data, ML, and software processes. MLTRL is unique from the other recommended processes in many ways, and perhaps most importantly because it considers data flows and ML models in the context of larger systems. These isolated processes (that are specific to e.g. modeling in prototype development or data wrangling in application development) are synergistic with MLTRL because they can be used within each level of the larger lifecycle or framework. For example, the Bayesian modeling processes^36,58 we mentioned above are really useful to guide developers of probabilistic ML approaches. But there are important distinctions between executing these modeling steps and cycles in a well-defined prototyping environment with curated data and minimal responsibilities, versus a production environment riddled with sparse and noisy data, that interacts with the physical world in non-obvious ways, and can carry expensive (even hidden) consequences. MLTRL provides the necessary, holistic context and structure to use these and other development processes reliably and responsibly.
Also related to our work, Google teams have proposed ML testing recommendations²⁰ and validated the data fed into ML systems⁶². For NLP applications, typical ML testing practices struggle to translate to real-world settings, often overestimating performance capabilities. An effective way to address this is devising a checklist of linguistic capabilities and test types, as in ref. 17–interestingly their test suite was inspired by metamorphic testing, which we suggested earlier in Level 7 for testing systems AI integrations. A survey by Paleyes et al.⁴⁶ goes over numerous case studies to discuss challenges in ML deployment. They similarly pay special attention to the need for ethical considerations, end-user trust, and extra security in ML deployments. On the latter point, Kumar et al.⁶³ provide a table thoroughly breaking down new threat vectors across the whole ML deployment workflow (some of which we mentioned above). These works, notably the ML security measures and the quantification of an ML test suite in a principled way—i.e., that does not use misguided heuristics such as code coverage—are valuable to include in any ML workflow including MLTRL, and are synergistic with the framework we’ve described in this paper. These analyses provide useful insights, but they do not provide a holistic, regimented process for the full ML lifecycle from R&D through deployment. An end-to-end approach is suggested by Raji et al.⁶⁴, but only for the specific task of auditing algorithms; components of AI auditing are mentioned in Level 7, and covered throughout the review processes.
Sculley et al.⁵⁵ go into more ML debt topics such as undeclared consumers and data dependencies and go on to recommend an ML Testing Rubric as a production checklist²⁰. For example, testing models by a canary process before serving them in production. This, along with similar shadow testing we mentioned earlier, is common in autonomous ML systems, notably robotics and autonomous vehicles. They explicitly call out tests in four main areas (ML infrastructure, model development, features and data, and monitoring of running ML systems), some of which we discussed earlier. For example, tests that the training and serving features compute the same values; a model may train on logged processes or user input, but is then served on a live feed with different inputs. In addition to the Google ML Testing Rubric, we advocate metamorphic testing: a SWE methodology for testing a specific set of relations between the outputs of multiple inputs. True to the checklists in the Google ML Testing Rubric and in MLTRL, metamorphic testing for ML can have a codified list of metamorphic relations¹⁸.
In domains such as healthcare, there has been the introduction of similar checklists for data readiness—for example, to ensure regulatory-grade real-world-evidence (RWE) data quality⁶⁵— yet these are nascent and not yet widely accepted. Applying AI in healthcare has led to developing guidance for regulatory protocol, which is still a work in progress. Larson et al.⁶⁶ provide a comprehensive analysis for medical imaging and AI, arriving at several regulatory framework recommendations that mirror what we outline as important measures in MLTRL: e.g., detailed task elements such as pitfalls and limitations (surfaced on TRL Cards), clear definition of an algorithm relative to the downstream task, defining the algorithm “capability” (Level 5), real-world monitoring, and more.
D’amour et al.¹⁹ dive into the problem we noted earlier about model miscalibration. They point to the trend in machine learning to develop models relatively isolated from the downstream use and larger system, resulting in underspecification that handicaps practical ML pipelines. This is largely problematic in deep learning pipelines, but we have also noted this risk in the case of causal inference applications. Suggested remedies include stress tests—empirical evaluations that probe the model’s inductive biases on practically relevant dimensions—and in general the methods we define in Level 7.
MLTRL has been developed, deployed, iterated, and validated in myriad environments, as demonstrated by the previous examples and many others. Nonetheless, we strongly suggest that MLTRL not be viewed as a cure-all for machine learning systems engineering. Rather, MLTRL provides mechanisms to better enable ML practitioners, teams, and stakeholders to be diligent and responsible with these technologies and data. That is, one cannot implement MLTRL in an organization and turn a blind eye to the many data, ML, and integration challenges we’ve discussed here. MLTRL is analogous to a pilot’s checklist, not autopilot.
MLTRL is intended to be complementary to existing software development methodologies, not replace or alter them. Specifically, whether the team uses agile or waterfall methods, MLTRL can be adopted to help define and structure phases of the project, as well as the success criteria of each stage. In the context of the software development process, the purpose of MLTRL is to help the team minimize the technical dept and risk associated with the delivery of an ML application by helping the development team ask necessary questions.
We discussed many data challenges and approaches in the context of MLTRL and should highlight again the importance of data considerations in any ML initiative. The data availability and quality can severely limit the ability to develop and deploy ML, whether MLTRL is used or not. It is again the responsibility of the ML practitioners, teams, and stakeholders to gather, use, and distribute data in safe, legal, and ethical ways. MLTRL helps do so with rigor and transparency, but again is not a solution for data bias. We recommend these recent works on data bias in ML^{67,68,69,70,71}. Further, ML/AI ethics is a continuously evolving, multidisciplinary space—see ref. 5. MLTRL aims to prioritize ethics considerations at each level of the framework, and would do well to also evolve over time with the broader ML/AI ethics developments.
We have described Machine Learning Technology Readiness Levels (MLTRL), an industry-hardened systems engineering framework for robust, reliable, and responsible machine learning. MLTRL is derived from the processes and testing standards of spacecraft development, yet lean and efficient for ML, data, and software workflows. Examples from several organizations across industries demonstrate the efficacy of MLTRL for AI and ML technologies, from research and development through productization and deployment, in important domains such as healthcare and physics, with emphasis on data readiness amongst other critical challenges. Our aim is for MLTRL works in synergy with recent approaches in the community focused on diligent data readiness, privacy and security, and ethics. Even more, MLTRL establishes a much-needed lingua franca for the AI ecosystem, and broadly for AI in the worlds of science, engineering, and business. Our hope is that our systems framework is adopted broadly in AI and ML organizations, and that “technology readiness levels” become common nomenclature across AI stakeholders— from researchers and engineers to sales-people and executive decision-makers.
At the end of each stage is a dedicated review period: (1) present the technical developments along with the requirements and their corresponding verification measures and validation steps, (2) make key decisions on path(s) forward (or backward) and timing, and (3) debrief the process (Debriefing (or: retrospectives, formal inquiry, final report, etc.) is a common process, used to improve future performance of projects in project management. See the following reference for more details⁷². MLTRL should include regular debriefs and meta-evaluations such that process improvements can be made in a data-driven, efficient way (rather than an annual meta-review). MLTRL is a high-level framework that each organization should operationalize in a way that suits their specific capabilities and resources.). As in the gated reviews defined by TRL used by NASA, DARPA, etc., MLTRL stipulates specific criteria for review at each level, as well as calling out specific key decision points (noted in the level descriptions above). The designated reviewers will “graduate” the technology to the next level, or provide a list of specific tasks that are still needed (ideally with quantitative remarks). After graduation at each level, the working group does a brief post-mortem; we find that a quick day or two pays dividends in cutting away technical debt and improving team processes. Regular gated reviews are essential for making efficient progress while ensuring robustness and functionality that meets stakeholder needs. There are several important mechanisms in MLTRL reviews that are specifically useful with AI and ML technologies: First, the review panels evolve over a project lifecycle, as noted below. Second, MLTRL prescribes that each review runs through an AI ethics checklist defined by the organization; it is important to repeat this at each review, as the review panel and stakeholders evolve considerably over a project lifecycle. As previously described in the levels definitions, including ethics reviews as an integral part of early system development is essential for informing model specifications and avoiding unintended biases or harm⁷³ after deployment.
In Fig. 2 we succinctly showcase a key deliverable: TRL Cards. The model cards proposed by Google⁷⁴ are a useful development for external user-readiness with ML. On the other hand, our TRL Cards are more information-dense, like datasheets for medical devices and engineering tools. These serve as “report cards” that grow and improve upon graduating levels and provide a means of inter-team and cross-functional communication. The content of a TRL Card is roughly in two categories: project info, and implicit knowledge. The former clearly states info such as project owners and reviewers, development status, and semantic versioning—not just for code, but also for models and data. In the latter category are specific insights that are typically siloed in the ML development team but should be communicated to other stakeholders: modeling assumptions, dataset biases, corner cases, etc. With the spread of AI and ML in critical application areas, we are seeing domain expert consortiums defining AI reporting guidelines—e.g., Rivera et al.⁷⁵ calling for clinical trial reports for interventions involving AI – which will greatly benefit from the use of our TRL reporting cards. We stress that these TRL Cards are key for the progression of projects, rather than documentation afterthoughts. The TRL Cards thus promote transparency and trust, within teams and across organizations. TRL Card templates will be open-sourced upon publication of this work, including methods for coordinating use with other reporting tools such as “Datasheets for Datasets”⁷⁶.
Identifying and addressing risks in a software project is not a new practice. However, akin to the MLTRL roots in spacecraft engineering, the risk is a “first-class citizen” here. In the definition of technical and product requirements, each entry has a calculation of the form risk = p(failure) × value, where the value of a component is an integer 1−10. Being diligent about quantifying risks across the technical requirements is a useful mechanism for flagging ML-related vulnerabilities that can sometimes be hidden by layers of other software. MLTRL also specifies that risk quantification and testing strategies are required for sim-to-real development. That is, there is nearly always a non-trivial gap in transferring a model or algorithm from a simulation testbed to the real world. Requiring explicit sim-to-real testing steps in the workflow helps mitigate unforeseen (and often hazardous) failures. Additionally, the comprehensive ML test coverage that we mention throughout this paper is a critical strategy for mitigating risks and uncertainties: ML-based system behavior is not easily specified in advance, but rather depends on the dynamic qualities of the data and on various model configuration choices²⁰.
We observe many projects benefit from cyclic paths, dialing components of technology back to a lower level. Our framework not only encourages cycles, but we also make them explicit with “switchback mechanisms” to regress the maturity of specific components in an ML/AI system:
Discovery switchbacks occur as a natural mechanism—new technical gaps are discovered through systems integration, sparking later rounds of component development⁷⁷. These are most common in the R&D phase for switching back one or two levels, and also a larger leap back across the “product handoff” gap from level 6 to 3—for example, a computer vision algorithm is only performant on a certain class of camera that is not necessarily available in production, so the algorithm must switchback to validate proof-of-concept on the lower grade camera.
Review switchbacks result from gated reviews, where specific components or larger subsystems may be dialed back to earlier levels. This switchback is one of the “key decision points” in the MLTRL project lifecycle (as noted in the Levels definitions) and is often a decision driven by business needs and timing rather than technical concerns (for instance when mission priorities and funds shift). This mechanism is common from levels 6/7 to 4, which stresses the importance of this R&D to the product transition phase (see Fig. 4 (left)).
Embedded switchbacks are predefined in the MLTRL process. Namely one switchback to move a proof-of-concept technology (at Level 4) back to proof-of-principle development (Level 2), and another for switching back from deployment (9) to proof-of-concept (4). In complex systems, particularly with ML and data-driven technologies, these built-in loops help mitigate technical debt and overcome other inefficiencies such as noncomprehensive V&V steps.
The three classes of switchbacks are described in Figs. 4, 3 and throughout the various application results. Without these built-in mechanisms for cyclic development paths, it can be difficult and inefficient to build systems of modules and components at varying degrees of maturity. Contrary to the traditional thought that switchback events should be suppressed and minimized, in fact, they represent a natural and necessary part of the complex technology development process—efforts to eliminate them may stifle important innovations without necessarily improving efficiency. This is a fault of the standard monotonic approaches in ML/AI projects, stage-gate processes, and even the traditional TRL framework.
It is also important to note that most projects do not start at Level 0; very few ML companies engage in this low-level theoretical research. For example, a team looking to use an off-the-shelf object recognition model could start that technology at Level 3, and proceed with thorough V&V for their specific datasets and use-cases. However, no technology can skip levels after the MLTRL process has been initiated. The industry default (that is, without implementing MLTRL) is to ignorantly take pretrained models, run fine tuning on their specific data, and jump to deployment, effectively skipping Levels 5–7. These patterns are shown in Fig. 4. Additionally, we find it advantageous to incorporate components from other high-TRL ranking projects while starting new projects; MLTRL makes the verification and validation (V&V) step straightforward for integrating previously developed ML components.
As suggested earlier, much of the practical value of MLTRL comes at the transition between levels. More precisely, MLTRL manages these oft-neglected transitions explicitly as evolving teams, objectives, and deliverables. For instance, the team (or working group) at Level 3 is mostly AI Research Engineers, but at Level 6 is mixed Applied AI/SW Engineers mixed with product managers and designers. Similarly, the review panels evolve from level to level, to match the changing technology development objectives. What the reviewers reference similarly evolves: notice in the level definitions that technical requirements and V&V guide early stages, but at and after Level 6 the product requirements and V&V takeover—naturally, the risk quantification and mitigation strategies evolve in parallel. Regarding the deliverables, notably TRL Cards and risk matrices⁷⁸ (to rank and prioritize various science, technical, and project risks), the information develops and evolves over time as the technology matures.
By defining technology maturity in a quantitative way, MLTRL enables teams to accurately and consistently define their ML progress metrics. Notably industry-standard “objectives and key results” (OKRs) and “key performance indicators” (KPIs)⁷⁹ can be defined as achieving certain readiness levels in a given period of time; this is a preferable metric in essentially all ML systems which consist of much more than a single performance score to measure progress. Even more, a meta-review of MLTRL progress over multiple projects can provide useful insights at the organization level. For example, analysis of the time-per-level and the most frequent development paths/cycles can bring to light operational bottlenecks. Compared to conventional software engineering metrics based on sprint stories and tickets, or time-tracking tools, MLTRL provides a more accurate analysis of ML workflows.
A distinct advantage of MLTRL in practice is the nomenclature: an agreed-upon grading scheme for the maturity of an AI technology, and a framework for how/when that technology fits within a product or system, enabling everyone to communicate effectively and transparently. MLTRL also acts as a gate for interpretability and explainability–at the granularity of individual models and algorithms, and more crucially from a holistic, systems standpoint. Notably, the DARPA XAI (DARPA Explainable Artificial Intelligence (XAI)) program advocates for this advance in developing AI technologies; they suggest interpretability and explainability are necessary at various locations in an AI system to be sufficient for deployment as an AI product, otherwise leading to issues with ethics and bias.
How to design a reliable system from unreliable components has been a guiding question in the fields of computing and intelligence⁸⁰. In the case of ML/AI systems, we aim to build reliable systems with myriad unreliable components: noisy and faulty sensors, human and AI error, and so on. There is thus significant value to quantifying the myriad uncertainties, propagating them throughout a system, and arriving at a notion or measure of reliability. For this reason, although MLTRL applies generally to ML/AI methods and systems, we advocate for methods in the class of probabilistic ML, which naturally represent and manipulate uncertainty about models and predictions²⁵. These are Bayesian methods that use probabilities to represent aleatoric uncertainty, measuring the noise inherent in the observations, and epistemic uncertainty, accounting for uncertainty in the model itself (i.e., capturing our ignorance about which model generated the data). In the simplest case, an uncertainty-aware ML pipeline should quantify uncertainty at the points of sensor inputs or perception, prediction or model output, and decision or end-user action—McAllister et al.²⁶ suggest this with Bayesian deep learning models for safer autonomous vehicle pipelines. We can achieve this sufficiently well in practice for simple systems. However, we do not yet have a principled, theoretically grounded, and generalizable way of propagating errors and uncertainties downstream and throughout more complex AI systems—i.e., how to integrate different software, hardware, data, and human components while considering how errors and uncertainties propagate through the system. This is an important direction for our future work.
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study. For the presented examples reflecting other studies, datasets may be found in the corresponding links or references provided in the text (if applicable). Please contact the corresponding author(s) with questions or concerns.
Code sharing is not applicable to this article as no code was produced. Otherwise, implementation materials for this work are included in an open-source repository at https://github.com/ai-infrastructure-alliance/mltrl, along with a full MLTRL home at ai-infrastructure.org/mltrl).
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The authors would like to thank Gur Kimchi, Carl Henrik Ek, and Neil Lawrence for valuable discussions about this project.
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NASA Frontier Development Lab, Mountain View, CA, USA
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A.L. conceived of the original ideas and framework, with significant contributions towards improving the framework from all co-authors. A.L. initiated the use of MLTRL in practice, including the neuropathology test case discussed here. C.G.-L. contributed insight regarding causal AI, including the section on the counterfactual diagnosis. C.G.-L. also made significant contributions broadly to the paper, notably in the methods descriptions and extensive paper revisions. Si.G. contributed to the spacecraft test case, along with early insights into the framework definitions. A.V. contributed to the definition of later stages involving deployment (as did A.G.), and comparison with traditional software workflows. Both E.X. and Y.G. provided insights regarding AI in academia, and Y.G. additionally contributed to the uncertainty quantification methods. Su.G. and D.L. contributed to the computer vision test case. A.G.B. contributed to the particle physics test case and significant reviews of the write-up. A.S. contributed insights related to causal ML and AI ethics. D.N. provided valuable feedback on the overall framework, and contributed significantly with the details on “switchback mechanisms”. S.Z. contributed to multiple paper revisions, with emphasis on clarity and applicability to broad ML users and teams. J.P. contributed to multiple paper revisions, and to deploying the systems ML methods broadly in practice for Earth and space sciences—same goes for C.M., with additional feedback overall on the methods. All co-authors discussed the content and contributed to editing the manuscript.
Correspondence to Alexander Lavin.
The authors declare no competing interests.
Nature Communications thanks Neil D. Lawrence, Julian Togelius, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Lavin, A., Gilligan-Lee, C.M., Visnjic, A. et al. Technology readiness levels for machine learning systems. Nat Commun 13, 6039 (2022). https://doi.org/10.1038/s41467-022-33128-9
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DOI: https://doi.org/10.1038/s41467-022-33128-9
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