Models (Preview)

Oracle AI Data Platform Workbench enables you to develop and refine machine-learning (ML) models that you can use for running inferences and predictions for your data.

Machine-learning models in AI Data Platform Workbench enable you to build optimized models through performance analysis, collaboration, and analysis of experimental conditions like hyper-parameters, input datasets, and feature engineering.

The Model registry in AI Data Platform Workbench exists in the Master Catalog and experiment runs developed in your workspace can be selected and registered as models. Models in the registry can be loaded into notebooks to compare performance against models in development or scheduled through workflows to run batch inferences using the loaded model.

You can register models in your AI Data Platform Workbench workspace by creating one based on an existing experiment or by uploading a file that contains the necessary information. You can also register a model directly from an experiment in your notebook.

Note:

If you haven't used previously used Experiments or Models in your AI Data Platform Workbench, you need to either restart your associated compute cluster or create a new one to use with Experiments and Models.

Limitations

Models are currently not supported by ARM-based compute clusters. Ensure the attached compute cluster is either Intel- or AMD-based.

Register a Model from an Existing Experiment Run (Preview)

You can register a model from an existing experiment run in your Oracle AI Data Platform Workbench workspace.

  1. On the home page, click Models.
  2. Click Register.
  3. Enter a name, description, and version for your model.
  4. Select Existing experiment run.
  5. From the Workspace drop-down list, select the workspace where your experiment is located.
  6. From the Experiment drop-down list, select the experiment to use for your model.
  7. From the Experiment run drop-down list, select the experiment run to use.
  8. From the Models drop-down list, select the model to use.
  9. Optional: Provide additional metadata in the form of free-form or defined tags. Click Add to create additional tags.
  10. Optional: Provide additional information in the form of custom fields, like model type or use cases. Click Add to create additional custom fields.
  11. Click Register.

View Model Details (Preview)

You can see details for your registered models, like versions, metrics, artifacts, and lineages.

  1. On the home page, click Models.
  2. Click the name of the model you want to view details for.
  3. Click the Overview, Details, Versions, or Artifacts tabs to see more details for your model.
  4. Click Versions, then click the Run name to see the run and experiment that produced this model.

Edit Model Details (Preview)

You can modify the name, description, and metadata tags for your models after creation.

  1. On the home page, click Models.
  2. Next to the model you want to edit, click Edit. You can also click the name of the model and click the Actions menu in the top right, then click Edit.
  3. Modify the model name or description. You can also add and remove tags. Click Save changes.

Delete a Model (Preview)

You can delete models you no longer need from your workspace.

  1. On the home page, click Models.
  2. Next to the model you want to delete, click Delete. You can also click the name of the model and click the Actions menu in the top right, then click Delete.
  3. Click Delete.