A Model Registry is a centralized repository or database that data scientists use to manage their machine learning models. It contains metadata about each model, such as its version, owner, input and output schema, timestamp, tags, and stage in its lifecycle (such as development, staging, production, or archived). This tool helps data science teams in managing the complete lifecycle of their ML models such as model deployment, model versioning, and model monitoring, etc.
Model Registry in practice
In a Model Registry, ML models are grouped by their unique names with multiple versions for each model. Each model version is linked to a code, parameters, metadata, and metrics, which help in tracking the model's origin and its performance. The registry also records the stage of each model version, indicating whether it's in development, staging, in production, or archived.
When a new model or a version of an existing model needs to be added, it is registered with all necessary metadata. Each version of the model is typically linked to the exact code, metrics, and parameters that were used to train the model. This allows for full traceability of how a model was created and why it behaves as it does.
The model registry acts as a collaboration platform for data science teams. Team members can comment on, discuss, and collaborate on models and their versions. It can also integrate with your existing CI/CD pipeline to automatically update model versions and stages, which makes it easy to automate model deployment and monitoring.
Through a model registry, teams can enforce governance policies and meet audit and compliance requirements. They can also flag models that behave unexpectedly and thus need to be retrained or replaced.
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