About
This skill simplifies the machine learning lifecycle by automating the setup of experiment tracking environments. It intelligently detects or suggests tools like MLflow or Weights & Biases (W&B), handles package installation and environment configuration, and generates the necessary boilerplate code for logging metrics, parameters, and artifacts. Whether starting a new model or integrating tracking into an existing pipeline, this skill ensures reproducibility and streamlined performance comparison with minimal manual effort.