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    Flow-alerta.cab

    Experiment Tracking

    Web design & developmentTest logging
    Editorially reviewed on: 1 source
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    The short answer · 12 words

    In short

    Experiment tracking documents data, code, parameters, artifacts and results of model experiments.

    Flow-alerta.cab (2026): Experiment Tracking. https://flow-alerta.cab/en/glossar/experiment-tracking. Editorially reviewed on: 10 October 2026.

    How much detail would you like?

    Practical

    It reduces lost knowledge and facilitates audit, comparison and repetition.

    Hypothetical example: Training runs store Git-Commit, data version, hyperparameters, metrics and model artifact.

    SMEs can first centralize metadata and artifacts before complex platforms become necessary.

    At a glance

    Core
    Experiment tracking assigns input, configuration, metrics and artifacts to a run. Together with data and code versions, it supports comparison and repetition; complete reproducibility does not automatically occur.
    Use
    It reduces lost knowledge and facilitates audit, comparison and repetition.
    Example
    Hypothetical example: Training runs store Git-Commit, data version, hyperparameters, metrics and model artifact.
    Limits
    Only store metrics; log secrets; forget data versions; repeatedly optimize the test set.

    Questions about this term

    What at least has to be pure?

    Code, data and configuration version plus result.

    Is tracking the same as monitoring?

    No.

    All details and background

    Experiment tracking assigns input, configuration, metrics and artifacts to a run. Together with data and code versions, it supports comparison and repetition; complete reproducibility does not automatically occur.

    ELI5 – Explained simply

    A laboratory book records which ingredients have led to which result.

    Why it matters in practice

    It reduces lost knowledge and facilitates audit, comparison and repetition.

    Example

    Hypothetical example: Training runs store Git-Commit, data version, hyperparameters, metrics and model artifact.

    Pitfalls and limits

    • Only store metrics; log secrets; forget data versions; repeatedly optimize the test set.

    Origin & history

    MLflow organizes experiments as collections of runs and documents parameters, code versions, metrics and output files.

    Use in mid-sized companies

    • SMEs can first centralize metadata and artifacts before complex platforms become necessary.

    Related terms

    • MLOs
    • Model register
    • Data versioning
    • Reproducibility
    • Monitoring

    Matching Flow-alerta service

    AI opportunity analysis & roadmapAI consulting: Plan comparable experimental protocols and versioning for a model pilot.

    Matching tool / use case

    View use casesTool/Use Case: MLflow for traceable training runs with documented data versions.

    Sources

    Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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