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.

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.

Manasi Vartak, founder and CEO of Verta, conceived of the idea of the open source project ModelDB database as a way to track versions of machine models while she was still in grad school at MIT. After she graduated, she decided to expand on that vision to build a product that could not only track model versions, but provide a way to operationalize them and Verta was born. Today, that company emerged from stealth with a $10 million Series A led by Intel Capital with participation from General Catalyst, who also led the company's $1.7 million seed round. Beyond providing a place to track model versioning, which ModelDB gave users, Vartak wanted to build a platform for data scientists to deploy those models into production, which has been difficult to do for many companies. She also wanted to make sure that once in production, they were still accurately reflecting the current data and not working with yesterday's playbook.

Manasi Vartak, founder and CEO of Verta, conceived of the idea of the open-source project ModelDB database as a way to track versions of machine models while she was still in grad school at MIT. After she graduated, she decided to expand on that vision to build a product that could not only track model versions, but provide a way to operationalize them -- and Verta was born. Today, that company emerged from stealth with a $10 million Series A led by Intel Capital with participation from General Catalyst, which also led the company's $1.7 million seed round. Beyond providing a place to track model versioning, which ModelDB gave users, Vartak wanted to build a platform for data scientists to deploy those models into production, which has been difficult to do for many companies. She also wanted to make sure that once in production, they were still accurately reflecting the current data and not working with yesterday's playbook.