Training and inference are two fundamental phases in the deep learning lifecycle. Training refers to the process where a model learns from a dataset by adjusting its parameters. During this phase, the model analyzes input data, makes predictions, compares these predictions to the actual outcomes, and then updates its parameters to reduce prediction errors. This iterative process continues until the model's performance reaches an acceptable level on the training data. Examples of common tasks in training include tasks like image classification, where a model might learn to identify objects in pictures based on labeled examples.
Inference, on the other hand, is the phase where the trained model is put to use in real-world applications. During inference, the model receives new input data and makes predictions based on what it learned during training. This phase does not involve further learning or parameter updates; instead, it relies solely on the knowledge the model has already acquired. For instance, once a model is trained to recognize cats in images, inference would involve feeding the model new images to determine whether they contain cats or not.
The main difference between the two phases lies in their objectives and processes. Training requires substantial computational resources and time because it involves numerous iterations to fine-tune the model’s parameters. In contrast, inference is typically much quicker because it involves running the trained model on new data without any further adjustments. This distinction is crucial for developers when designing systems, as they must optimize both training for accuracy and inference for speed, especially in applications requiring real-time predictions, such as autonomous driving or real-time image recognition.