Snowflake ML
Prototype to production machine learning with distributed GPUs or CPUs on the same platform as your governed data. Streamline model development and MLOps with no infrastructure to maintain or configure — all through a centralized UI.
Easy
Integrated end-to-end model pipelines with any open source model on the same platform where your data lives
Efficient
Large-scale training and inference with scalable CPUs or GPUs without infrastructure to configure or maintain
Trusted
Discover, manage, use and govern ML features and models in Snowflake across the ML lifecycle with built-in lineage and monitoring

Scalable Model Development
Easily train models with distributed GPUs or CPUs from Snowflake Notebooks on Container Runtime. Use pre-installed, popular libraries such as XGBoost and PyTorch, or pip install any package from open source hubs such as PyPi and HuggingFace.
Continuously Updated ML Features
Create, manage and serve ML features with continuous, automated refresh on batch or streaming data using the Snowflake Feature Store.


Flexible Model Inference
Deploy models for production with distributed GPUs or CPUs from the Snowflake Model Registry, including support for models trained on external platforms. Easily monitor performance and drift metrics with integrated ML Observability.
Explore more related features:
End-to-end ML in Snowflake

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3Only available in select regions. See documentation for full program details.