Machine Learning with Databricks and MLflow
This course teaches you to build, track, and deploy machine learning models on the Databricks platform using MLflow. You start with the reproducibility crisis in ML — understanding why untracked experiments, scattered notebooks, and missing version control create production failures — and learn how MLflow solves these problems with structured experiment tracking, model versioning, and artifact management. You then explore MLflow's architecture in depth: the Tracking layer for logging parameters, metrics, and artifacts; the Model Registry for governance and stage gates; and the Projects
Skills you'll learn
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