Best Practices for Experiment Tracking in MLOps

Experiment tracking is presented as essential infrastructure for production machine learning and LLM systems, providing a centralized, reproducible record of each training run’s configurations, metrics, artifacts, code version, execution environment, data and feature lineage, resource use, and links to model registry and deployment stages. Effective systems support scalable storage, distributed training, CI/CD automation, access controls, audit trails, run comparison, and integration with monitoring so teams can evaluate candidates, detect regressions, retrain models, and meet governance requirements. LLM workflows require additional tracking of prompts, sampling parameters, tokenizer and base-model versions, fine-tuning artifacts, quality evaluations, and substantial compute costs. The text distinguishes experiment qualification from runtime deployment control, describing model registries and tools such as LaunchDarkly as mechanisms for gradual exposure, monitored rollouts, and rapid rollback. It also warns against local-only records, missing lineage, overwritten runs, incomplete metrics, absent environment details, and disconnected registries, while noting that lightweight tracking may be sufficient for purely temporary exploration that will not affect deployment or long-term decisions.

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