MLOps
Model registry
If you’re a data scientist or developer, you can use the model registry to manage your machine learning models, along with all metadata associated with their creation: parameters, performance metrics, artifacts, logs, and more. For more information, see the model registry documentation.
Model experiments
As a data scientist, when you create machine learning models, you often experiment with different parameters, configurations, and feature engineering to improve the performance of the model. Keeping track of all this metadata and the associated artifacts so that you can later replicate the experiment is not trivial. With machine learning experiment tracking, you can log parameters, metrics, and artifacts directly into GitLab, which provides easy access to these things later on.
For details, see the model experiments documentation.
GitLab MLOps Python client
GitLab offers a Python client to interact with the GitLab MLOps features.
For details, see the GitLab MLOps Python client documentation.