Blog
Build, Deploy and Serve Models at Scale with Snowflake ML
Read about the latest generally available announcements for building and deploying models at scale in Snowflake.
Featured Capabilities
Aramark
SnowflakeMachine Learning
Accelerate machine learning from prototype to production 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.
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 at scale with a fully integrated platform that brings together tools, workflows and 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.
ML Workflow
Accelerate the lifecycle of development to production with Snowflake ML
Model Development
Build scalable models on Snowflake data
Optimize data loading and distribute model training from Snowflake Notebooks on Container Runtime 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 for production-grade pipelines
Create, manage and serve ML features with continuous, automated refresh on batch or streaming data using the Snowflake Feature Store. Promote discoverability, reuse and governance of features across training and inference.
Production
Deploy ML models built anywhere for low-latency inference
Log models built anywhere in Snowflake Model Registry and serve for real-time or batch predictions on Snowflake data with distributed GPUs or CPUs.
- Easily monitor performance and drift metrics with integrated ML Observability.
Feature overview
Learn more about the integrated features for development
and production in Snowflake ML
Contact Snowflake
Ready to get started?
Derek SayboltAccount Executive, Snowflake
Michael YoungPrincipal Solution Engineer, Snowflake
Paul LorenzenSales Development, Snowflake
