What is the difference between training and inference in a machine learning context?

In the context of machine learning, the correct answer accurately describes two fundamental phases of the model lifecycle: training and inference. Training is the process where a model learns from a dataset by adjusting its weights and biases based on the input data and the corresponding output labels. During training, the model identifies patterns and relationships in the data, effectively "teaching" itself to make accurate predictions.

Inference, on the other hand, refers to the phase where the trained model is deployed to make predictions on new, unseen data. At this stage, the model takes input data and uses its learned parameters to generate outputs, which could be classifications, predictions, or any other type of decision-making based on the training it has undergone.

This distinction is crucial because each phase serves different purposes in the machine learning workflow. Training focuses on the internal mechanics of model learning, while inference emphasizes the application of that learned knowledge in real-world scenarios. Understanding this difference is fundamental for anyone working in AI and machine learning, as it influences how models are developed, validated, and utilized post-deployment.