Model management
Summarize
Summary of Model management
Model management in the NLU Workbench enables you to oversee the entire life cycle of your Natural Language Understanding (NLU) models. This process includes building, testing, publishing, and tuning your models to ensure they meet your business needs. The phases you use depend on your model’s application, such as Virtual Agent or AI Search, and the system displays only the relevant options accordingly. Proper NLU plugins, including NLU Workbench - Advanced Features and Intent Discovery, are required to access full capabilities like model testing and performance monitoring.
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Creating and Building Models
To create NLU models, navigate to NLU Workbench > Models and select the appropriate application tab. You can create models by copying prebuilt models, importing CSV files with training data, or starting from scratch. After creation, build your model by adding and managing:
- Intents: Define user requests your model can understand.
- Entities: Extract contextual details from user inputs.
- Vocabulary: Incorporate business-specific terms and acronyms.
- Test sets: Add utterances with expected intents for evaluation.
Train the model with realistic utterances to improve recognition accuracy.
Testing, Publishing, and Tuning Models
Test your model’s performance using the NLU Workbench - Advanced Features to identify improvement areas. Once testing meets your standards, publish the model to enable its use in applications like Virtual Agent.
If using Virtual Agent models with the advanced features installed, you can further tune your model through the Expert Feedback Loop, which integrates real user utterances to enhance model accuracy. For Issue Auto Resolution models, tuning is accessed via the IAR tab.
Model Settings
The Settings page allows you to update your model’s name, description, and confidence threshold. Adjusting the confidence threshold controls how certain the model must be before predicting an intent, helping you balance precision and recall based on your business requirements.
Additional Features
The Irrelevance detection feature helps keep Virtual Agent conversations focused by training the model to recognize and avoid responding to irrelevant utterances, improving the overall user experience.
Manage your NLU model's life cycle in the NLU Workbench. Model management phases guide you through the iterative process of building, testing, and publishing your model.
Bringing your NLU model from creation to deployment requires multiple steps, separated into phases. You can return to earlier phases when you want to adjust and maintain your model.
The phases available for your model depend on the model's application. The system will display a phase, button, or function only when it applies to your model's application.
Create a model
To create a model for Virtual Agent or AI Search, navigate to . The Virtual Agent tab opens by default. Select the appropriate tab for the model you want to create.
- Use prebuilt model: Copy one of the included read-only models, and add content specific to your business.
- Import data from CSV: Upload a CSV file that contains training utterances and matched intents.
- Start from blank: Go through the process of setting up a new model from scratch.
To get started, see Creating models.
Model management phases
After creating a model, access its management phases by navigating to . Select the tab for your model's application, then the name of the model to open the Model details page on the model overview.
There are three phases on a Virtual Agent model's overview page: Build and train your model, Test and publish your model, and Tune your model. These phases guide you as you build and improve your model.
Build and train your model
Build the model by adding and managing content:- Intents: Add more intents to broaden the range of user requests that your model can understand.
- Entities: Add more entities so that your model can extract more contextual details from your users' requests.
- Vocabulary: Add vocabulary to enable the model to better understand words and phrases that are specific to your business, such as industry terms and acronyms.
- Test set: Add test utterances and their expected intents to your model's default test set.
To learn more, see Build and train your model.
Train your model using utterances that the model is likely to encounter from your users. To learn more, see Train and try your NLU model.
Test and publish your model
Test your model to gauge the performance and identify areas for improvement.
For more information on testing and thresholds, see Test and publish your model.
When you're satisfied with the results of testing, publish your model to make it available for use by other applications. For more information, see Publish your NLU model.
Tune your model
If NLU Workbench - Advanced Features is installed, and your model is created for Virtual Agent, the Tune your model phase is enabled. With this phase, you can use Expert Feedback Loop to incorporate actual user utterances into your model.
For more information, see Tune your model.
If your model is created for Issue Auto Resolution, you will be taken to IAR Tuning by selecting the name of your model in the IAR tab of the NLU Workbench homepage. For more information, see Issue Auto Resolution Tuning in NLU.
Model settings
Use the Settings page of the model overview to change the name and description of the model. You can also modify the confidence threshold of the model. The confidence threshold determines how confident the model must be to predict an intent.
For more information, see NLU model settings.