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.

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.

See the ModelDB frontend in action: ModelDB is an end-to-end system to manage machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB can be used with any ML environment via the ModelDB Light API. ModelDB native clients can be used for advanced support in and . The ModelDB frontend provides rich summaries and graphs showing model data.

BOSTON – Sam Madden, professor of electrical engineering and computer science at MIT, is hoping to help advance the field of machine learning from dark art to principled science with an open source project. ModelDB, available on GitHub, is essentially a database system designed to help organize and manage machine learning models. "These models are the engines of machine learning," Madden said at the MassIntelligence conference, hosted by MassTLC and MIT's Computer Science and Artificial Intelligence Laboratory. "They are the things that take the data and extract the insight out of it." When researchers build machine learning models, the process is highly iterative.

Moreover, we found that data scientists usually had an ML environment of choice (e.g., scikit-learn, R, spark.ml), As a result, we implemented native logging libraries for different ML environments that would capture models built by a data scientist along with pre-processing operations performed on the data (e.g., one-hot-encoding, scaling). As of now, we have written logging libraries for scikit-learn and spark.ml. Libraries for different ML environments implement a ModelDB thrift interface that is used to communicate with the backend.