MLflow 3.0 - AI and MLOps on Databricks

Discover how to streamline your machine learning lifecycle in this 48-minute conference talk exploring MLflow 3.0 on Databricks. Learn to manage everything from experimentation to production deployment with enhanced efficiency and improved results through comprehensive tracking, evaluation, and deployment capabilities for both traditional ML models and cutting-edge generative AI applications. Master automatic experiment tracking to compare model performance across different runs and configurations. Explore robust model monitoring techniques throughout the entire lifecycle across various environments. Understand deployment management with sophisticated versioning and governance controls to ensure reliable model releases. Implement proven MLOps workflows that span all development stages from initial experimentation to production scaling. Build and deploy generative AI applications at enterprise scale using Databricks' integrated platform capabilities. Gain practical techniques for accelerating ML development cycles while maintaining quality and reliability standards. Whether you're new to MLOps or an experienced practitioner, acquire actionable strategies to optimize your machine learning operations and deployment processes through real-world demonstrations and best practices shared by Databricks software engineers Arpit Jasapara and Corey Zumar.