Ace the AWS AI Practitioner 2025 Exam – Unleash Your Cloud Genius!

The process that occurs when a model analyzes a new image to identify objects is known as inference. Inference is the phase where a pretrained model makes predictions based on unseen data. During this step, the model applies the knowledge it has gained during training—such as patterns, features, and relationships it has learned from the training dataset—to evaluate new data.

When a model is deployed and subjected to new images, it does not learn or adjust its parameters; instead, it simply uses the weights and biases acquired during the training phase to derive outputs, such as identifying objects within the images. This is a key distinction, as training involves modifying the model based on data to improve its performance while inference utilizes that trained model to perform predictions.

The other options represent different stages or processes in the machine learning workflow. For example, training involves the iterative process of learning from a dataset, model deployment refers to putting the trained model into a usable environment where it can be accessed and evaluated, and bias correction focuses on identifying and mitigating any biases present in the model or data that could affect its performance. None of these processes specifically pertain to the analysis of new data for prediction, which is the essence of inference.