Models

Overview

Train datasets into explainable models.
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Models are created by training jobs.

Training uses a dataset, a workspace, a training configuration, optional model constraints, and a processing compute configuration. The result is a model that can be inspected, evaluated, and deployed.

How model training works

1

Dataset

Training starts from a dataset created by onboarding.

2

Workspace

A workspace provides the context for training jobs, models, and their metadata.

3

Configuration

A training configuration defines how induction should train the model.

4

Constraints

Model constraints can guide which modules are included, excluded, or prioritised during training.

5

Job

A training job runs the configuration against the dataset using a processing compute configuration.

6

Model

The completed job creates the model used for inspection, evaluation, and deployment.

Model training flow

Model training flow.

Training with induction

The platform currently trains models through induction.

Induction builds an explainable model from a prepared dataset. In most cases, the default training configuration is enough to start. Tune the configuration or add constraints when you need more control over model complexity, feature interactions, or training behaviour.