Technology

MLflow

MLflow is the open-source platform for managing the complete machine learning lifecycle: tracking, reproducibility, and deployment.

MLflow standardizes the ML workflow using four core components. **MLflow Tracking** logs experiment details, recording parameters, metrics (like accuracy), and artifacts for every run. **MLflow Projects** package code in a reusable format, ensuring reproducibility across environments via a simple `mlflow run` command. **MLflow Models** provide a consistent model format ('flavor') for deployment, supporting frameworks like scikit-learn, PyTorch, and TensorFlow. Finally, the **MLflow Model Registry** centralizes model management, handling versioning and stage transitions (e.g., Staging to Production) for governance and collaboration.