Model Lab

Model Lab turns prepared datasets into explainable models. A training job learns from a dataset using saved training settings and compute resources. Its output is a trained model that you can inspect and deploy.

How the resources fit together

A training configuration defines how the model learns. A compute configuration defines where the job runs. Keeping them separate lets you reuse settings and understand what changed between runs.

From training to evaluation

Start with an available dataset and a workspace, then prepare the training settings and run a job. Inspect the output model and its resource references before deploying it. The Training overview explains this flow.

A trained model and a deployed model are distinct resources. Serving creates the running deployment used for queries. Model Lab > Model Evaluation evaluates that deployment, so deployment comes before evaluation even though evaluation is grouped in Model Lab.

Inspect structure and behaviour

A model’s learned structure includes modules, partitions and decision rules. Structure explains how the model is organised; attributions explain how its parts contributed to a particular prediction. In the app, use Explanation Lab to explore these views after deployment.

When comparing training runs, retain the dataset, configuration, constraints and job references. Compare evaluation results using the same data population and settings so that the differences are meaningful. See Trained models, Evaluate models and Explanation views.

Next steps

Create or choose a workspace, then read how training works.