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Understanding Concepts

What is a Concept?

Concepts are a known entity in the world. They are fundamental to annotating your data, for defining the vocabulary that a model should output, for relating things to each other, for receiving your predictions, searching for these concepts and more.
For example, a concept may be a "dog", a "cat", a "tree", etc. So if you annotate some input data as having a "dog" or "cat" present, that provides the foundation for training a model on that data. A model could then be created with "dog" and "cat" in it's list of concepts it will learn to predict. After training, the model could predict the concepts "dog" and "cat" and you could search over all your data for "dog"'s and "cat"'s that the model finds or that have been annotated.
A Data concept is an abstract or generic idea generalized from your data that helps you build AI applications around that concept. A example of an concept includes
  • Products
  • Product Attribute
  • Businesses
  • Customer Behavior

How Do I Add A Concept?

You can easily leverage our language, text and time series models by simply adding a concept on our concept.
Last modified 6mo ago