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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.
Use Case
End-to-end machine 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.
“Previously, the process to train all these models and generate predictions took a half hour. The unified model on Snowflake is super quick; we’re talking minutes to generate forecasts for hundreds of thousands of customers. This speed and simplicity will help unlock additional capabilities for the business like simulation and scenario forecasting.”
Dan Shah
Manager of Data Science
Feature overview
Learn more about the integrated features for development
and production in Snowflake ML
Get Started
Take the next stepwith Snowflake
Start your 30-day free Snowflake trial today
- $400 in free usage to start
- Immediate access to the latest Snowflake ML features
- Build and deploy a model with CPUs or GPUs
End-to-end ML
FAQs
Can I use both CPUs and GPUs for ML workflows in Snowflake?
Yes, data scientists and ML engineers can build and deploy models with distributed processing in CPUs or GPUs. This is enabled by the underlying container-based infrastructure that powers the Snowflake ML platform.
How do I get started building models on Snowflake data?
You can build features and models directly from Snowflake Notebooks, or through any IDE of choice with ML Jobs.
Can I only run models in production that are built in Snowflake?
No, you can bring models built anywhere externally to run in production on Snowflake data. During inference, you can take advantage of integrated MLOps features such as ML observability and RBAC governance.
Is Snowflake ML compatible with open source?
Yes, Snowflake ML is fully compatible with any open-source library. Securely access to open source repositories via pip and bring in any model from hubs such as Hugging Face.
How does pricing work for Snowflake ML?
Snowflake operates on a consumption-based pricing model with the latest credit pricing table here.
Can I try Snowflake ML for free?
Yes, you can try any of our ML quickstarts directly from the free trial experience.
