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

Effective ML and LLM experiment tracking extends beyond hyperparameters to include data versions, code commits, environments, model artifacts, prompt templates, tokenizer and sampling settings, evaluation results, resource usage, and the reasons runs were triggered. It distinguishes experiment tracking at the individual run level, model tracking across deployment stages, data tracking for training inputs and lineage, and prompt tracking for LLM inference behavior, arguing that gaps among these records make production regressions difficult to reproduce or diagnose. A scalable tracking system should use durable metadata and artifact storage, automated CI/CD logging for successful and failed runs, evaluation gates linked to model registries, and production monitoring that feeds drift, latency, cost, and quality signals back into triage or retraining. LaunchDarkly AgentControl is presented as a runtime configuration layer that versions prompts, model parameters, and tools, connects offline validation to live configurations, and supports controlled rollouts, targeting, A/B testing, model switching, and rapid rollback without redeployment. Together, these practices create an auditable chain from an experiment and its inputs to the configuration served to users, enabling teams to release changes more safely and investigate incidents more quickly.

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