Jennifer Flynn had a problem. 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.
Data scientists struggle to keep track of which version of a ML model works best—a problem that grows when multiple models are involved—and even when a model is deployed, companies often have nothing in place to monitor its performance.
Verta is an ML model management platform that tracks versions of models and data, can run multiple experiments simultaneously to find the best performing data-model combination and monitors those models and the data once they are deployed.
Vartak created Verta to commercialize ModelDB, which helps data scientists make sense of their ML models.
As data scientists develop machine-learning models, they go through many iterations and often test multiple iterations simultaneously.
Data scientists are not experts in registering models or data; they are also not experts in building containers or putting things inside containers and making sure that they run on various platforms.
After using Verta, the company pushes major upgrades monthly.
“We now have time to make even better models to better serve our customers.”
It’s the sort of thing that Verta and similar open-source tools hope to conquer to make data science more accessible”.
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