Presented by Meeta Dash – VP Product at Verta
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