ML Experiment Tracking Setup

概要

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.

主な機能

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• Automated MLflow and W&B initialization and configuration
• Environment setup including dependency installation and variables
• Generation of boilerplate code for logging metrics and parameters
• Support for model artifact management and tracking
• Context-aware tool selection based on project requirements

ユースケース

• Automating the transition between local MLflow and cloud-based W&B tracking
• Initializing experiment tracking for a new machine learning project
• Integrating MLOps best practices into an existing model training pipeline