ML Experiment Tracking: What to Track Across Models, Data, and Production

Experiment tracking is presented as essential infrastructure for production machine learning and LLM systems, replacing fragmented records with a reproducible history of each training run’s configurations, metrics, artifacts, code version, execution environment, dataset and feature lineage, resource use, and downstream model-registry status. Effective systems centralize metadata through resilient, access-controlled tracking servers, use scalable storage for metrics and large artifacts, integrate with version control, feature stores, CI/CD pipelines, monitoring, and model registries, and support comparison, search, distributed logging, cost visibility, and governance. For LLMs, tracking must additionally capture prompts, sampling settings, base models, tokenizer versions, fine-tuning artifacts, evaluation rubrics, and serving quality and cost signals. Automated evaluation gates can qualify candidates for registration, while runtime controls such as LaunchDarkly feature flags and AgentControl can govern gradual exposure, monitor live performance, and roll back problematic versions without redeployment. The discussion emphasizes that lineage, immutable records, environment capture, and audit logs are especially important for regulatory compliance and troubleshooting, warns against local-only storage, missing data references, overwritten runs, and incomplete metrics, and notes that lightweight tracking may be sufficient for disposable exploration but becomes necessary when models affect users, business decisions, safety, or compliance.

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