- Comparing keyword-based and machine learning topic detection
Pega Platform offers two types of topic models that you can use interchangeably, depending on your needs. Learn more about the differences between keyword-based and machine learning topic models and when to use them.
- Creating keyword-based topic models
Efficiently connect your customers with the right consultant without having to provide training data to the topic model. Instead, you can use a list of topic-specific keywords to train the model.
- Creating machine learning topic models
Efficiently connect your customers with the right consultant by providing training data to a topic model.
- Connecting to topic models in Cloud AutoML
Broaden your selection of topic models in Pega Platform by connecting to models that you create in Cloud AutoML, Google's cloud-based machine learning service.
- Connecting to topic models through an API
Broaden your selection of topic models in Pega Platform by connecting to custom models through an API. Train and deploy your topic models, and expose an API endpoint to allow Pega Platform to interact with the models.
- Best practices for creating categorization models
Use categorization analysis to assign labels to text. In Pega Platform, you can categorize text into topics, sentiments, and intents.
- Requirements and best practices for creating a taxonomy for rule-based classification analysis
The right classification of data in a taxonomy makes relevant information more accessible, which can have various practical applications. This information can help you address customer support requests in a timely manner or gather feedback on your products.