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Model Versioning with MLflow: Tracking and Managing Your ML Models
- MLflow is an open-source platform for managing the complete machine learning lifecycle, including tracking experiments, managing model versions, and deploying models.
- MLflow provides tools like Tracking, Projects, Models, and Registry to facilitate the transition from experimentation to production.
- MLflow Tracking allows logging of parameters, metrics, artifacts, source code, and environments for machine learning experiments.
- The MLflow Tracking UI enables browsing experiments, inspecting parameters and metrics, and comparing runs visually.
- MLflow supports visualization through a Chart View to compare metrics like accuracy, loss, or AUC across runs.
- Comparing multiple runs side-by-side in MLflow helps in selecting the best-performing model from a batch of experiments.
- With MLflow's Model Registry, models can be registered, assigned version numbers, promoted through stages, and managed collaboratively.
- MLflow supports real-world use cases like MLOps Pipelines, experiment governance, and model rollbacks.
- The MLflow UI can be launched locally to visualize and manage machine learning experiments and models.
- MLflow offers a production-ready solution for transparent experiment tracking, visual comparison tools, and controlled model registry and versioning.
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