What role do APIs play in deploying machine learning models?

APIs, or Application Programming Interfaces, play a crucial role in deploying machine learning models by enabling applications to interact with these models for predictions. Once a model is trained, it is often encapsulated within an API, allowing developers to send data to the model through standardized requests. The API then processes the input data and returns predictions or results, which can be easily integrated into various applications.

This functionality is essential for making machine learning accessible and usable in real-world scenarios. By providing a straightforward interface, APIs facilitate communication between different software components, allowing for seamless integration of machine learning capabilities into applications without requiring detailed knowledge of the underlying model. This makes it possible for various systems and platforms to leverage sophisticated models for tasks such as classification, regression, recommendation, and more, enhancing the overall application experience for users.

Other options do not accurately reflect the API's role in model deployment. Storing data used for training the model pertains more to data management aspects, generating training data relates to the data preparation and model training phase, and visualizing model performance focuses on evaluation metrics rather than direct interaction for predictions.