Accelerating ML Workflows with Databricks

Tyler’s presentation will take you on a journey through cutting-edge innovations in machine learning workflows and model management. Whether you’re an experienced data scientist, an ML engineer, or just diving into the world of AI, this session will equip you with the tools and techniques to streamline processes, enhance productivity, and maintain robust model performance. Prepare to uncover actionable insights and best practices that will simplify even the most complex challenges in the field.

• End-to-End Experiment Tracking: Gain insights into logging runs, parameters, metrics, and artifacts with MLflow. Learn how this comprehensive system ensures reproducible comparisons across experiments, fostering transparency and collaboration in machine learning workflows.
• Unified Feature Store & Serving: Discover how to streamline model development by building scikit-learn pipelines, registering models, and deploying them effortlessly as REST or SQL endpoints. Say goodbye to managing complex infrastructure with this out-of-the-box solution designed for scalability and ease of use.
• Proactive Drift Monitoring: Explore cutting-edge techniques for maintaining model performance. Learn how to automatically profile inference data, detect distribution shifts, and trigger timely alerts using Lakehouse Monitoring. Empower your team to stay ahead of challenges with early detection and adaptive responses.