MIT spinout Verta offers tools to help companies introduce, monitor, and manage machine-learning models safely and at scale. Companies today are incorporating artificial intelligence into every corner of their business. The trend is expected to continue until machine-learning models are incorporated into most of the products and services we interact with every day. As those models become a bigger part of our lives, ensuring their integrity becomes more important. That's the mission of Verta, a startup that spun out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

Companies today are incorporating artificial intelligence into every corner of their business. The trend is expected to continue until machine-learning models are incorporated into most of the products and services we interact with every day. As those models become a bigger part of our lives, ensuring their integrity becomes more important. That's the mission of Verta, a startup that spun out of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Verta's platform helps companies deploy, monitor, and manage machine-learning models safely and at scale.

WIRE)--Verta, the Operational AI company, today released findings from the 2023 AI/ML Investment Priorities study, which surveyed more than 460 AI and machine learning (ML) practitioners to benchmark AI/ML spending plans across industry sectors in light of evolving technology trends, industry developments, and macroeconomic conditions. The study was conducted by Verta Insights, the research practice of Verta Inc., and found that nearly two-thirds of organizations are planning to either increase or maintain their spending on AI/ML technology and infrastructure despite economic headwinds in the broader market. "We currently are experiencing an inflection point for the AI/ML industry, with technologies like ChatGPT and Stable Diffusion driving heightened interest in how companies can leverage machine learning models to significantly automate human-based activities with very innovative and game-changing capabilities. Findings from our research study confirm that organizations are continuing to make significant investments in AI/ML technology and talent, despite turbulence in the market, as they orient their business strategies around creating intelligent experiences for their customers," said Conrado Silva Miranda, Chief Technology Officer of Verta. In the research study, 31% of respondents said that their organizations would increase AI/ML spending in 2023 due to the current economic conditions, while 32% said that they would maintain 2022 spending levels for AI/ML technology and infrastructure.

PALO ALTO, Calif., Sept. 13, 2022 -- Verta Inc., a leading provider of enterprise model management and operational artificial intelligence (AI) solutions, today released findings from the 2022 State of Machine Learning Operations study, which surveyed more than 200 machine learning (ML) practitioners about their use of AI and ML models to drive business success. The study was conducted by Verta Insights, the research practice of Verta Inc., and found that although companies across industries are poised to significantly increase their use of real-time AI within the next three years, fewer than half have actually adopted the tools needed to manage the anticipated expansion. In fact, 45% of the survey respondents reported that their company reported having a data or AI/ML platform team in place to support getting models into production, and just 46% have an MLOps platform in place to facilitate collaboration across stakeholders in the ML lifecycle, suggesting that the majority of companies are unprepared to handle the anticipated increase in real-time use cases. The survey also revealed that just over half (54%) of applied machine learning models deployed today enable real-time or low-latency use cases or applications, versus 46% that enable batch or analytical applications. However, real-time use cases are set for a sharp increase, according to the study.

Jennifer Flynn had a problem. Shortly after joining LeadCrunch as a senior data scientist, she wanted to push out one small update of the company's software, which uses machine learning to find sales leads for its business customers. The data science team consisted of just five engineers, including her. That simple update took days and required help from the company's product development team, too. "It wasn't tenable," Flynn said, now LeadCrunch's principal data scientist.

According to the 2020 Gartner Hype Cycle for Artificial Intelligence, machine learning (ML) is entering the Trough of Disillusionment phase. This is the phase where the real work begins--best practices, infrastructures, and tools are being developed to facilitate the technology's integration into real-world production environments. Today, ML technologies have secured a central role in many companies. ML technologies also are beginning to gain footholds across industries as they become more widely adopted in enterprises. For example, advances in speech and natural language models are fueling growth in voice applications.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel's Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture. Please see the Roundup Of Machine Learning Forecasts And Market Estimates, 2020 for additional market research on A.I. and machine learning. The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today: Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel's Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture. The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today: Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise.

Bottom Line: Verta helps enterprises track the thousands of machine learning models they're creating using an integrated platform that also accelerates deploying models into production, ensuring that models' results are based on the most current data available. The same is true for all data-intensive businesses today. Despite ramping up their data science teams and investing in the latest machine learning tools, many struggle to keep models organized and move them out of development and into production. Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform where they can be launched into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work to define the first open-source system for managing machine learning models.

Stephen Foskett discusses the practicalities involved in packaging, deploying, and operating AI models with Manasi Vartak of Verta. Deploying an AI model in production is a challenge, just like it was in the past with software. Once a company has an AI model to deploy, they must validate its results, create scaffolding code to make it consumable, optimize the data pipelines, instrument it, and assign operators. This is what Manasi and Verta have developed, and the world of AIOps parallels that of DevOps but with some unique twists. The data component of AI models presents a unique challenge not found in some other enterprise applications, and it is important to continually test the model to ensure that it hasn't drifted off target as data changes. Previously, training models was the main challenge for AI, but now it's all about getting things into production. That's why we started this podcast and why we created AI Field Day! This episode features: Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen's writing at GestaltIT.com and on Twitter at @SFoskett Manasi Vartak, CEO and Founder of Verta (@VertaAI). Find Manasi on Twitter at @DataCereal Date: 10/20/2020 Tags: @SFoskett, @DataCereal, @VertaAI