_index

---
stage: ModelOps
group: MLOps
info: To determine the technical writer assigned to the Stage/Group associated with this page, see <https://handbook.gitlab.com/handbook/product/ux/technical-writing/#assignments>
title: Machine learning model experiments
---

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- Offering: GitLab.com, GitLab Self-Managed, GitLab Dedicated

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- [Introduced](https://gitlab.com/groups/gitlab-org/-/epics/9341) in GitLab 15.11.
- [Generally available](https://gitlab.com/groups/gitlab-org/-/epics/9341) in GitLab 17.8.

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As you create machine learning models, you likely experiment with different parameters, configurations,
and feature engineering to improve the model's performance. To replicate your experiments later, you need
to effectively track the metadata and artifacts. Use GitLab model experiments to track and log parameters,
metrics, and artifacts directly into GitLab.

## What is an experiment?

In a project, an experiment is a collection of comparable model runs.
Experiments can be long-lived (for example, when they represent a use case), or
short-lived (results from hyperparameter tuning triggered by a merge request),
but usually hold model runs that have a similar set of parameters measured
by the same metrics.

![Model experiment table showing models and their number of runs, creator and last activity.](img/experiments_v17_9.png)

## Model run

A model run is a variation of the training of a machine learning model, that can be eventually promoted to a version
of the model.

![Experiment run list table with run ID, creation timestamps, creator name, and status.](img/runs_v17_9.png)

The goal of a data scientist is to find the model run whose parameter values lead to the best model
performance, as indicated by the given metrics.

![Experiment details and metadata page showing ML flow run ID, source information, and parameters.](img/run_v17_9.png)

Some example parameters:

- Algorithm (such as linear regression or decision tree).
- Hyperparameters for the algorithm (learning rate, tree depth, number of epochs).
- Features included.

## Track new experiments and runs

Experiment and trials can only be tracked through the
[MLflow](https://www.mlflow.org/docs/latest/tracking.html) client compatibility.
See [MLflow client compatibility](mlflow_client.md) for more information
on how to use GitLab as a backend for the MLflow Client.

## Explore model runs

To list the current active experiments, either go to `https/-/ml/experiments` or:

1. On the top bar, select **Search or go to** and find your project.
1. Select **Analyze** > **Model experiments**.
1. To display all runs that have been logged, along with their metrics, parameters, and metadata, select an experiment.
1. To display details for a run, select **Details**.

## View log artifacts

Trial artifacts are saved as packages. After an artifact is logged for a run, all artifacts logged for the run are listed in the package registry. The package name for a run is `ml_experiment_<experiment_id>`, where the version is the run IID. The link to the artifacts can also be accessed from the **Experiment Runs** list or **Run detail**.

## View CI information

You can associate runs to the CI job that created them, allowing quick links to the merge request, pipeline, and user that triggered the pipeline:

![Run detail page displaying associated CI job, pipeline, and user information.](img/run_detail_ci_v17_9.png)

## View logged metrics

When you run an experiment, GitLab logs certain related data, including its metrics, parameters, and metadata. You can view the metrics in a chart for analysis.

To view logged metrics:

1. On the top bar, select **Search or go to** and find your project.
1. Select **Analyze** > **Model experiments**.
1. Select the experiment you want to view.
1. Select the **Performance** tab.

![A graph of an experiment's performance](img/metrics_v17_10.png)