MLflow: an Open Source Tool for Managing the ML Project Lifecycle
summary
MLflow is presented as an open-source lifecycle tool that tracks and manages ML experiments: a Python notebook trains a RandomForest on the Iris dataset while autolog captures parameters, metrics, and artifacts to a tracking server. The pipeline shows comparing hundreds of runs, promoting best runs into a versioned model registry with staging/production tags and approvals, exporting models as multi-flavor packages or Docker/Python artifacts, and deploying them to Databricks or SageMaker to load for reproducible predictions.
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chapters (9)
- 0:00Introduction
- 1:02Software vs ML lifecycle
- 3:08What is MLflow
- 4:10MLflow components
- 5:56Tracking demo
- 8:42Model deployment
- 10:47Registries and governance
- 12:33Production usage
- 25:21Q&A
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