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Explore advanced MLOps practices for implementing production-grade machine learning workflows on Databricks in this comprehensive conference talk. 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. Learn about 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 environments, CI/CD pipeline automation for continuous deployment, and GenAI application evaluation and streamlined deployment. Presented by Arpit Jasapara, Software Engineer at Databricks, and Eric Golinko, Staff Data Scientist at Databricks, this 76-minute session provides in-depth insights into building scalable, production-ready machine learning systems using Databricks' comprehensive MLOps platform.
