April 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.