From Notebook to Production: End-to-End MLOps on Databricks
FORMAT: WorkshopLEVEL: IntermediateLANGUAGE: Spanish
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.
