Arpit Jasapara

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

Ready to streamline your ML lifecycle? Join us to explore MLflow 3.0 on Databricks, where we'll show you how to manage everything from experimentation to production with less effort and better results. See how this powerful platform provides comprehensive tracking, evaluation, and deployment capabilities for traditional ML models and cutting-edge generative AI applications. Key takeaways: - Track experiments automatically to compare model performance - Monitor models throughout their lifecycle across environments - Manage deployments with robust versioning and governance - Implement proven MLOps workflows across development stages - Build and deploy generative AI applications at scale Whether you're an MLOps novice or veteran, you'll walk away with practical techniques to accelerate your ML development and deployment.