In the context of AWS, what does the term "inference" refer to?

Inference in the context of AWS and machine learning refers to the process of using a trained model to make predictions or decisions based on new, unseen data. After a model has completed the training phase, where it learns patterns and relationships from historical data, inference allows the model to apply what it has learned to evaluate new inputs and generate outputs. This stage is crucial as it translates the insights gained during training into actionable results.

For example, once a model has been trained to recognize images of cats and dogs, inference would involve inputting new images into the model to see how accurately it can classify them as either a cat or a dog based on the knowledge it acquired during training. This makes inference an essential step in deploying machine learning in real-world applications, allowing businesses to derive value from their data by making informed predictions or decisions.

The other options describe different aspects of the machine learning lifecycle, but they do not capture the specific process of prediction on new data, which is the fundamental aspect of inference.