Experiment Tracking
Experiment tracking is a practice and set of tools used in machine learning and data science to systematically record, organize, and compare experiments. It involves logging parameters, metrics, code versions, datasets, and outputs to ensure reproducibility and facilitate collaboration. This helps teams manage iterative development, avoid redundant work, and make data-driven decisions about model performance.
Developers should learn experiment tracking when working on machine learning projects, especially in research, production model development, or team environments. It is crucial for reproducing results, comparing different model configurations, debugging failures, and maintaining audit trails for compliance. Use cases include hyperparameter tuning, A/B testing of algorithms, and collaborative projects where multiple team members run experiments.