Building and operationalizing machine learning models at scale requires a seamless and efficient workflow. In this session, we’ll explore how Snowflake ML enables data scientists and ML engineers to prepare data, create features, train models, and deploy them for inference—all within Snowflake's unified platform. You'll learn how to use the Snowflake Feature Store for automated feature management, train models on CPUs or GPUs using open-source frameworks like scikit-learn, XGBoost, and LightGBM in Snowflake Notebooks with Container Runtime, and deploy models for scalable inference with the Snowflake Model Registry. We’ll also cover ML Observability, Explainability, and ML Lineage to ensure full visibility into your model’s performance and data provenance.