The Snowflake Model Registry is the feature designed to store, version, and manage machine learning models as first-class schema-level objects within Snowflake, regardless of where or how the model was originally trained. It has built-in support for models created with popular third-party frameworks (scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Hugging Face, MLFlow pyfunc, etc.), allowing them to be logged, versioned, and deployed inside Snowflake while tracking associated metadata such as metrics, tags, and lineage. This directly satisfies the need for centralized version control and metadata tracking of an externally developed model. ML Observability focuses on monitoring deployed models' performance and drift, Container Runtime for ML provides compute infrastructure for training/serving workloads, and the Feature Store manages reusable feature data rather than model artifacts and versions.
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