Simplifying MLOps with Model Registry


To effectively deploy and scale ML models across the development pipeline requires a mix of machine learning, software engineering, and operational skills which is rare to find in a single person or even in a single team. Additionally, organizations with hundreds of models today face the unique challenge arising from the heterogeneity in ML workflows and the siloed nature of these teams.

In this talk, we will discuss the whys and hows around streamlining model release and model management using a Registry:

· Ensure model reproducibility & portability across local, dev, and prod environments

· Build transparency by creating a central source of truth for models across their lifecycle

· Establish best practices around managing model releases & workflows

· Enforce compliance and governance for models across risk categories


Manasi Vartak is the founder and CEO of Verta (, a tool that allows ML practitioners to rapidly version, deploy, and monitor enterprise ML models at scale. Manasi is the creator of ModelDB, the first open-source model management system deployed at Fortune-500 companies. She previously worked on deep learning for content recommendation as part of the feed-ranking team at Twitter and dynamic ad-targeting at Google. Manasi is passionate about building intuitive data tools, helping companies become AI-first, and figuring out how data scientists and the organizations they support can be more effective. Manasi has spoken at several top research and industry conferences such as SIGMOD, VLDB, SparkSummit, TWIML, Data Science Salon, and AnacondaCon, and has authored a course on model management. Manasi earned her MS/PhD in Computer Science from MIT.

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