Apr 16, 2025: Snowflake ML Jobs (Preview)¶

Snowflake announces the preview of Snowflake ML Jobs, a new capability that allows you to run machine learning (ML) workflows from your local environment.

Snowflake ML Jobs enable you to:

• Run ML workloads on Snowflake Compute Pools, leveraging GPU and high-memory CPU instances.
• Use your preferred development environment, such as VS Code or Jupyter notebooks, without requiring Snowflake worksheets or notebooks.
• Install and use custom Python packages within your runtime environment.
• Optimize data loading, training, and hyperparameter tuning with Snowflake’s distributed APIs.
• Integrate with orchestration tools, such as Apache Airflow.
• Monitor and manage jobs programmatically using Snowflake’s APIs.

Key benefits of Snowflake ML Jobs include:

• Scalability: Execute large-scale ML training on datasets requiring significant compute resources or GPU acceleration.
• Flexibility: Retain your existing development environment while leveraging Snowflake’s compute resources.
• Efficiency: Work directly with large Snowflake datasets to reduce data movement and avoid expensive data transfers.
• Productionization: Move ML code from development to production with minimal changes, enabling programmatic execution through pipelines.
• Compatibility: Lift and shift open-source ML workflows with minimal code modifications.

To get started with Snowflake ML Jobs, see Snowflake ML Jobs.