ExperimentTracking

Experiment tracking refers to the practice of recording, organizing, and managing information about experiments in order to enable reproducibility, auditing, and collaboration. It is used across scientific research and data-driven fields such as machine learning, analytics, and software experiments. A typical experiment-tracking system stores data in hierarchies of experiments and runs. An experiment represents a collection of related runs often tied to a research question, project, or dataset. A run corresponds to a single execution of a process, capturing parameter settings, code version, environment details, timestamps, and user.

Key data tracked include hyperparameters or configuration settings, metrics (evaluations, loss, accuracy), artifacts (models, datasets, plots), and links to code and data versions. Provenance and lineage information support reproducibility by enabling re-creation of results and understanding dependencies. Many systems provide dashboards and comparison views to assess multiple runs side by side.

Common workflows involve defining the hypothesis or objective, configuring and executing experiments, logging results automatically or manually, reviewing and comparing runs, and selecting or deploying the most promising models or findings. Automation is often integrated with machine learning pipelines, notebooks, and CI/CD to reduce manual work.

Benefits include improved reproducibility, collaboration, governance, and traceability for research and product development. Challenges include interoperability between tools, data privacy and security, storage requirements, and the overhead of diligent logging. Notable tools and platforms in this space include MLflow, Weights & Biases, Neptune, and DVC, among others.