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.
Efficient
Large-scale data processing for model development directly within Snowflake's platform, eliminating the need for data movement
Easy
ML workflows using any Python library and framework or low-code SQL functions with no infrastructure to configure or maintain
Trusted
Discover, manage, use, and govern ML features and models in Snowflake across the entire ML lifecycle with end-to-end lineage
Scalable Model Development
Easily build models training with distributed GPUs or CPUs from Snowflake Notebooks on Container Runtime. Leverage 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
Serve 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 metrics2.
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Bringing familiar modeling capabilities to Snowflake ML has enabled us to more rapidly iterate on our models, improving accuracy and operational efficiency
SVP of Engineering at Decile
Using Snowflake ML, we've been able to achieve a 70% cost reduction and enhanced agility by moving from running hour-long inference jobs to predictions in near real-time.
CTO, Paytronix
We can more easily build predictive models and mock up data products all in the Snowflake ecosystem because the data is all there.
Manager of Data Science, IGS Energy
SNOWFLAKE ML ARCHITECTURE
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1Private preview
2Public preview
3Only available in select regions. See documentation for full program details.