In this guide, you'll learn how to build a complete machine learning lifecycle in Snowflake, from model development to production deployment. You'll deploy HuggingFace models, train custom ML models, track experiments, deploy for inference, and enable real-time feature serving. The application addresses end-to-end ML development showing how to do audio processing, feature extraction, model training, deployment, and monitoring all inside Snowflake with unified governance across the application full-stack.
For prerequisites, environment setup and instructions, refer to the QuickStart Guide.