Manage a model in AI Builder
Creating the optimal model for your business can be a rather iterative process. Results may vary depending on the configurations you set and the training data you provide. Thus, updating either or both of those factors could improve your model performance. In some cases, however, there is a chance the performance may degrade. Each AI model type has a set of guidelines to help walk you through the process of creating the best model, tailored to your needs.
Evaluate your model
After you train your model for the first time, you can evaluate its performance and quality on its details page.
Depending on your AI model type, a performance score might appear for each version you have trained. You can use this score to quickly compare two versions of the same model. However, the score is based on the configuration for that training. It is important to take into consideration any changes made between versions when comparing scores.
Each AI model type has a different explanation for how the score is calculated, and how the score should be interpreted. View the tooltip next to Performance to learn more.
Some AI model types have the option to quickly test the results of your model version with real data of your choosing. Select Quick test to see your model in action.
After you finish evaluating your newly trained model, you have two options:
- Publish your model: For more information about when to publish a model, see When should I publish my model.
- Create a new version: For more information about when to create a new version, see When should I create a new version.
Edit model name
- At the top of the page, select Settings.
- In the Model settings pane on the right, under Name, enter a different name. Depending on your AI model type, you may need to first select the General section.
- Select Save to finalize the change.
Create a new version
To create a new version, select New version at the top of the page.
You can have up to two trained versions available at a time—one Published version, and one that is not published, Last trained version. If you train a new version when a last trained version already exists, the existing last trained version is overwritten.
When you create a new version, your model is based on the configuration from an existing version—your published version, or your last trained version. If you have both, you have to choose which one you want to create the new version from.
A new version is created only after you have successfully trained it. If you leave without finishing your changes and training your model, your progress is saved as a draft. Certain actions, such as creating a new version or retraining, may be disabled until you train or discard your draft. You can only have one draft available at a time, so you have to select Resume draft to pick up where you left off, or Discard draft to get rid of the changes before you can continue.
After training, your training results appear under the Last trained version section of the Details page.
If you are satisfied with your last trained version, you can publish your model to make it available. Otherwise, you can always create a new version. For more information about publishing your model, see When should I publish my model?
When should I create a new version?
You can create a new version of your model to help improve the model performance or quality. This depends on the AI model type, where some models can be improved by updating the configuration and some models can be improved by updating the training data.
Due to the experimental nature of machine learning, not all new versions you create will have an increase in model performance. If you are not satisfied with your model, you can create a new version to try to yield better results.
If you are satisfied with your model, you can publish your model to make it available. Similarly, because you can only have two trained versions available at a time, you can publish a version if you do not want it to be overwritten by a new version.
For more information about the nuances of improving your model performance, see the message underneath your performance score.
Retrain and republish existing models
Whereas training creates a new version by updating your configuration, retraining creates a new version using the same configuration as your current version. The benefit of retraining is that it will study any new data so that your model stays accurate over time. This action is only applicable to certain AI model types.
Sign in to PowerApps and then, in the navigation pane, select AI Builder > Models.
Follow the steps for your model type:
Prediction and text classification models:
In the Performance section, select the … menu, and then select Retrain now.
This replaces your last trained version. Now, publish this version if ready.
Perform these steps on each of your AI Builder models to get your AI models up and running again.