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ml-flow

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End-to-end ML pipeline on Azure Machine Learning for heart disease prediction. Features 4-step automated workflow (data prep, training, evaluation, registration), MLflow experiment tracking, and managed endpoint deployment. Built with Azure ML SDK v2, scikit-learn, and auto-scaling compute clusters.

  • Updated Feb 13, 2026
  • Python

An end-to-end machine learning project implementing MLflow for comprehensive experiment tracking and model lifecycle management. This project demonstrates a complete ML pipeline using Random Forest Classification, showcasing MLflow's powerful capabilities in managing machine learning workflows.

  • Updated Oct 26, 2024
  • Jupyter Notebook

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