Eric Golinko

This in-depth session explores advanced MLOps practices for implementing production-grade machine learning workflows on Databricks. We'll examine the complete MLOps journey from foundational principles to sophisticated implementation patterns, covering essential tools including MLflow, Unity Catalog, Feature Stores and version control with Git. Dive into Databricks' latest MLOps capabilities including MLflow 3.0, which enhances the entire ML lifecycle from development to deployment with particular focus on generative AI applications. Key session takeaways include: - Advanced MLflow 3.0 features for LLM management and deployment - Enterprise-grade governance with Unity Catalog integration - Robust promotion patterns across development, staging and production - CI/CD pipeline automation for continuous deployment - GenAI application evaluation and streamlined deployment