Organizations today deploy thousands of ML models in production to serve personalized experiences, handle diverse customer segments, and maintain separate models across regions or business units.
This session explores how Hopsworks uses modularization and runtime parameterization to streamline feature/training and model management at scale.
You’ll learn how :
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Breaking pipelines into reusable components reduces duplication.
Injecting parameters at runtime eliminates the need for separate deployments per configuration.
Infrastructure complexity can be dramatically reduced through smarter pipeline design.
You can scale from dozens to thousands of models without breaking your deployment pipeline.
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