David Hung

Deploying machine learning models into production typically involves automated CI/CD pipelines. After experimentation, data scientists commit finalized code, initiating pipelines that replicate development steps on production data for consistency and scalability. This session introduces how Snowflake ML is designed to simplify ML workflow deployment by enabling remote execution directly from orchestration engines, including Snowflake Tasks. Come to this session to learn more about how you can productionize your end-to-end ML pipelines within Snowflake and configure them to trigger model retraining runs and visualize and monitor task execution graphs.