Experiment tracking is the practice of systematically recording everything needed to reproduce and compare a machine-learning training run: hyperparameters, dataset version, code commit, environment (library versions, GPU type), training metrics over time, evaluation metrics on held-out sets, saved model artifacts, and run metadata (who, when, why). Without it, ML teams lose the ability to answer basic questions — which run produced the current production model, what changed between yesterday's good run and today's bad one, which hyperparameter swept best on the validation set — and quickly degrade into folklore engineering where the current best model is whichever one is sitting on someone's laptop.
The 2026 landscape has stabilised around a handful of platforms. **MLflow** (open-source, Databricks-backed) is the de facto free baseline — straightforward tracking API, server for persistence, model registry included, works everywhere. **Weights & Biases** is the SaaS leader in usability and visualisation, preferred by research teams and well-funded startups. **Neptune** and **Comet ML** compete in the managed-tracking space with specific strengths (Neptune for regulated industries, Comet for model production management). **Vertex AI Experiments** and **SageMaker Experiments** ship bundled with cloud ML platforms. Hugging Face's **model card and dataset card** specs overlap where tracking meets documentation.
For APAC mid-market teams, the right progression is **start with MLflow, upgrade when you feel friction**. MLflow running on a shared server (or Databricks-managed) handles the first 10-30 active experiments comfortably. Upgrade to Weights & Biases or Neptune when the team hits ~3+ active ML engineers, experiment count exceeds roughly 500/quarter, or visualisation needs grow beyond MLflow's UI. Regardless of tool, enforce a naming convention, required tags (project, owner, intent), and mandatory eval metrics per run so dashboards and queries actually work.
The non-obvious failure mode is **tracking metrics but not artifacts**. A team logs every metric meticulously, picks the best run six weeks later, and discovers the actual model weights weren't saved — only the loss curves were. The 'best run' cannot be promoted because it no longer exists. Configure tracking to save model artifacts, tokenizer / preprocessor configs, training data splits, and seed values alongside metrics, with retention policies that match your promotion window. A run that cannot be reproduced or redeployed is not a tracked experiment — it is a line on a graph.
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