From Notebook to Production: End-to-End MLOps on Databricks

Is your Machine Learning model trapped in a Notebook or does it actually generate value in production? Taking ML models into the real world requires more than just good training code; it demands a solid MLOps strategy. In this hands-on workshop, we will transform a use case from scratch into an industrial-grade solution using Databricks and MLflow. Through a hands-on approach and using Databricks Free Edition, attendees will master the complete lifecycle (End-to-End) under professional standards. Workshop Agenda: - Industrialization Fundamentals: Introduction to Lakehouse architecture, MLflow as an industry standard, and the role of the Feature Store in reproducibility. - Engineering and Governance: Creating a Feature Store and managing raw data with best practices. - Scalable Training: Developing models with exhaustive experiment tracking and a bonus on distributed training for large data volumes. - Deployment Strategies: Analysis of trade-offs between Batch Inferencing and Real-time Serving (Model Serving). We will implement "Deploy Code" and "Deploy Artifacts" patterns. - Modern Operationalization: Professional orchestration through Databricks Asset Bundles (DABs), the definitive tool for infrastructure as code on Databricks. - The finishing touch (CI/CD): How to integrate everything into a continuous deployment pipeline to guarantee quality across multiple environments. Outcome for attendees: Upon completion, each participant will have the source code, infrastructure configuration, and a productized, orchestrated ML model ready to be replicated in real environments.