AWS Machine Learning Blog
Amazon Comprehend now supports resource tagging for custom models
Amazon Comprehend customers are solving a variety of use cases with custom classification and entity type models. For example, customers are building classifiers to organize their daily customer feedback into categories like “loyalty,” “sales,” or “product defect.” Custom entity models enable customers to analyze text for their own terms and phrases, such as product IDs from their inventory system. Amazon Comprehend removed the complexity from creating these models. All that’s required is a CSV file with labels and example text.
Because of this big step forward in ease of use, more employees across more teams are creating custom models for their projects. With this proliferation of more models across more teams, you need to be able to itemize usage and costs associated with each model for internal billing and usage management.
With this release, you can now assign resource tags to Amazon Comprehend custom classifier and custom entity models. Tagging these resources helps identify, track, and itemize their usage and costs. For example, there might be one model for sales text analysis and another model for marketing text analysis. With the resource tagging feature, you can provide the tab label on the custom model resource when you create your new models using either the SDK or with no code in the AWS Management Console. When usage and billing gets generated against the model, you can see usage and costs itemized using these resource tags.
You can add resource tags during custom model creation. The following example shows how to add tags to a custom model while you’re preparing to train the model.
To learn more about tagging custom classifiers and custom entity types, read Custom Comprehend.
About the author
Nino Bice is a Sr. Product Manager leading product for Amazon Comprehend, AWS’s natural language processing service.