What is inference in the context of AI models?

Inference in the context of AI models refers to the process of using a trained model to make predictions or generate content based on new input data. During inference, the model applies the knowledge it learned during the training phase to analyze the input and provide an output, such as a classification, recommendation, or even creative content generation.

This is fundamentally different from other phases of the AI model lifecycle, such as training the model, where the model learns patterns from data, or the evaluation phase, where the model's performance is measured using various metrics. Inference is specifically about leveraging a pre-trained model to produce results from real-world data as it comes in. It is essential for practical applications of AI, as it represents the phase where users interact with and benefit from the capabilities of the model.