Managing Machine Learning Experiments with MLflow

Discover how to effectively manage machine learning experiments using MLflow in this 56-minute conference talk from the Toronto Machine Learning Series. Learn from Databricks experts Jules S. Damji, a Developer Advocate and MLflow contributor, and Brooke Wenig, a Machine Learning Practice Lead, as they introduce MLflow, an open-source project designed to address the challenges of reproducing and sharing ML experiments, managing models, and preparing them for production. Gain insights into overcoming common obstacles in the machine learning pipeline, including reproducibility, version comparison, and model rollback. Explore techniques for enhancing collaboration among data scientists and streamlining the process of making models production-ready.