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Module 3: Experiment Tracking

🎯 Learning Objectives​

By the end of this module, you will be able to:

  • Set up and use MLflow for comprehensive experiment tracking
  • Implement Weights & Biases for advanced experiment management
  • Compare experiments effectively and select the best models
  • Ensure reproducibility across all experiments

📚 Module Content​

Chapter 1: MLflow Introduction​

Components of MLflow (Tracking, Projects, Models, Registry), setup, and logging experiments.

Chapter 2: Weights and Biases​

W&B features, sweeps for hyperparameter tuning, artifacts, reports, and team collaboration.

Chapter 3: Comparing Experiments​

Metrics visualization, hyperparameter analysis, experiment selection, and reproducibility.

⏱️ Estimated Time​

ActivityDuration
Reading2.5 hours
Hands-on Exercises2 hours
Quiz30 minutes
Total5 hours

🛠️ Tools Covered​

  • MLflow Tracking Server
  • Weights & Biases
  • Neptune.ai (overview)
  • TensorBoard

🚀 Let's Begin!​

Start with Chapter 1: MLflow Introduction.