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From ingesting and processing data to analyzing and modeling it, to building and sharing data and AI applications, Snowflake helps you innovate faster and do more with your data.
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FEATURE
Snowflake ML
Accelerate machine learning with distributed GPUs or CPUs on the same platform as your governed data. Streamline model development and MLOps for real-time and batch workflows with no infrastructure to maintain or configure — all through a centralized UI.
Overview
Piecing together many tools for ML workflows can be complex. Get models ready for production on one platform.
Develop, deploy and monitor ML features and models with a fully integrated platform that brings together tools, real-time and batch workflows, and scalable compute infrastructure to the data.
Integrate development and MLOps
Unify model pipelines end to end with any open source model on the same platform where your data lives.
Scale models out of the box
Scale ML pipelines over CPUs or GPUs with built-in infrastructure optimizations — no manual tuning or configuration required.
Generate trusted ML insights
Discover, manage and govern features and models in Snowflake across the entire lifecycle.
Accelerate development to productionwith Snowflake ML
Model Development
Build scalable models on Snowflake data with agentic ML workflows
- Autonomously generate, iterate and refine fully executable ML pipelines from natural language prompts using Cortex Code.
Optimize data loading and distribute model training from Snowflake Notebooks or any IDE of choice with ML Jobs.
- Use pre-installed libraries such as XGBoost and PyTorch, or pip install any package from open source hubs such as PyPi and HuggingFace.
Feature Management
Develop and manage features in batch and real time for production-grade pipelines
Create, manage and serve ML features with continuous, automated refresh on batch or streaming data in under 30 milliseconds using the Snowflake Feature Store.
Promote discoverability, reuse and governance features across training and inference.
Easily search for and visually trace features across the pipeline via the integrated Feature Store UI.
Production
Deploy ML models built anywhere for batch and online inference
- Log models built anywhere into Snowflake Model Registry, and serve them for batch or real-time predictions on Snowflake data with CPUs or GPUs.
Serve models in under 100 milliseconds to power low-latency, online use cases, such as personalized recommendations and fraud detection.
- Easily monitor performance and drift metrics with integrated ML Observability.
