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This skill streamlines the machine learning lifecycle by automating the setup of professional experiment tracking tools such as MLflow and Weights & Biases (W&B). It intelligently analyzes your project context, installs necessary dependencies, configures environment variables, and generates production-ready code snippets for logging metrics, parameters, and model artifacts. By standardizing the tracking process, it ensures that your experiments are reproducible and easy to compare, allowing you to focus on model performance rather than infrastructure setup.
