MLflow - Platform for Complete Machine Learning Lifecycle

Discover how the MLflow open source platform streamlines the machine learning development lifecycle in this 31-minute Devoxx conference talk. Learn to track experiment runs and results across frameworks, register projects for easy reproducibility, and efficiently productionize models using Docker containers, Azure ML, or Amazon SageMaker. Gain insights from Quentin Ambard, a Solutions Architect at Databricks, as he shares his experience from French startups and explains how MLflow addresses the complexities of ML development beyond traditional software lifecycles. Explore techniques for trying multiple algorithms, tools, and parameters while ensuring reproducibility, especially crucial for deploying models in production environments.