Explore the integration of MLflow and Azure Machine Learning for efficient ML lifecycle management in this 44-minute conference talk by Nishant Thacker from Databricks. Discover how this powerful combination addresses bottlenecks in ML projects and enhances MLOps capabilities on Azure. Learn about versioning, run history maintenance, production pipeline automation, cloud and edge deployment, and CI/CD pipelines. Gain insights into the ML lifecycle management process, MLflow components, and the MLflow model lifecycle. Follow along with a comprehensive demo that covers installing MLflow, setting up the API, importing SDKs, and working with Azure Machine Learning Workspace. Dive into practical aspects such as creating sample applications, utilizing MLflow API for experiments and tracking, building images, testing environments, registering models, and managing metrics and deployments. Understand the importance of governance and usage quotas in ML projects. By the end of this talk, grasp how MLflow and Azure Machine Learning synergize to streamline and strengthen the entire machine learning lifecycle. Read more