Experiment Tracking
Definition
Experiment tracking is the practice of systematically logging and organizing ML experiments (including parameters, metrics, artifacts, and code versions) to enable comparison, reproducibility, and informed decision-making.
Why It Matters
ML development is fundamentally iterative. You try dozens of hyperparameter combinations, data preprocessing approaches, and model architectures. Without systematic tracking, you lose the ability to understand what worked and why.
“Wait, which notebook had that good result?” “What learning rate did we use?” “Did we try this combination before?” These questions kill productivity. Experiment tracking transforms chaotic exploration into systematic research.
For AI engineers working with LLMs, experiment tracking extends to prompt engineering and RAG configurations. Different system prompts, retrieval parameters, chunk sizes, and model versions all need tracking. The combination space is enormous, and tracking is essential for making progress.
Implementation Basics
Effective experiment tracking captures:
1. Parameters Every configurable value that affects results. Hyperparameters (learning rate, batch size), data parameters (chunk size, overlap), model parameters (temperature, top_p), and infrastructure settings (GPU type, seed).
2. Metrics Quantitative results over time. Training metrics (loss curves, gradient norms), evaluation metrics (accuracy, F1, BLEU), and business metrics (latency, cost per request). Track both final values and time series.
3. Artifacts Model checkpoints, evaluation datasets, generated outputs, confusion matrices, sample predictions. These enable deeper analysis and reproducibility.
4. Environment Code version (git commit), dependencies (requirements.txt), compute specs (GPU type, memory). Critical for reproducibility, as results that can’t be reproduced are useless.
5. Comparisons Side-by-side visualization of runs. Filter by parameters, sort by metrics, group by experiments. The ability to ask “what’s different between run A and run B?” is crucial.
Popular tools: MLflow (open-source, self-hosted), Weights & Biases (feature-rich, cloud-hosted), Neptune, Comet, TensorBoard.
Start tracking immediately, even with simple logging to files. The cost of tracking is tiny compared to the cost of repeating work you’ve already done.
Source
MLflow Tracking provides an API and UI for logging parameters, code versions, metrics, and artifacts when running ML code, and for later visualizing and comparing results.
https://mlflow.org/docs/latest/tracking.html