{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Experiment Tracking — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"<div style=\"font-family:sans-serif;border:1px solid #e0e0e0;border-radius:8px;padding:16px;max-width:600px;background:#ffffff;color:#111111;\"><p style=\"margin:0 0 4px;font-size:11px;color:#666;\">AI Glossary — gaks.ai</p><h3 style=\"margin:0 0 8px;font-size:16px;\">Experiment Tracking</h3><p style=\"margin:0 0 12px;font-size:14px;line-height:1.6;\">The practice of systematically recording the details of each model training run, including hyperparameters, datasets, metrics, and results. Experiment tracking makes it possible to reproduce past results, compare approaches, and understand what changes led to improvements. It is foundational infrastructure for any team running experiments at scale.  See also: MLOps, hyperparameter, benchmark.</p><a href=\"https://gaks.ai/glossary/experiment-tracking\" style=\"font-size:12px;color:#0077aa;\">Source: gaks.ai/glossary/experiment-tracking →</a></div>"}