ML Experiment Tracking

Status: public · Confidence: medium (0.725) · Basis: verified_sources

## TL;DR

ML experiment tracking records runs, parameters, metrics, artifacts, code versions, and datasets so teams can compare model behavior and reproduce results.

## Core Explanation

Modern ML and LLM systems change across prompts, models, retrieval indexes, fine-tuning data, evaluation sets, and runtime settings. Experiment tracking gives those changes durable identifiers and comparable metrics.

For LLM evaluation and agent engineering, tracking is the bridge between a one-off result and a repeatable quality loop. It lets teams answer which model, prompt, dataset, retrieval setup, and grader produced a score.

## Source-Mapped Facts

- TensorFlow documentation says TensorBoard provides measurements and visualizations for machine learning workflows and enables tracking experiment metrics such as loss and accuracy. ([source](https://www.tensorflow.org/tensorboard/get_started))
- Weights and Biases documentation says experiment tracking workflows create a run, store hyperparameters in configuration, log metrics over time, and save run outputs. ([source](https://docs.wandb.ai/models/track))
- Google Cloud documentation says Vertex AI Experiments tracks experiment steps, inputs such as parameters and datasets, and outputs such as models, checkpoints, and metrics. ([source](https://docs.cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments))

## Further Reading

- [TensorBoard getting started](https://www.tensorflow.org/tensorboard/get_started)
- [Weights and Biases experiments](https://docs.wandb.ai/models/track)
- [Vertex AI Experiments](https://docs.cloud.google.com/vertex-ai/docs/experiments/intro-vertex-ai-experiments)