What is the difference between training and inference in deep learning?

Training and inference are two distinct phases in the lifecycle of a deep learning model. Training is the process where the model learns patterns from data by adjusting its internal parameters. This involves feeding the model labeled input data (e.g., images with corresponding categories), computing predictions, measuring errors using a loss function, and updating parameters via optimization algorithms like stochastic gradient descent (SGD). For example, a convolutional neural network (CNN) trained to classify images might iteratively adjust its filters to recognize edges, textures, and shapes. Training requires significant computational resources and time, often involving GPUs or TPUs to handle large datasets and complex architectures.

Inference, on the other hand, is the application of a trained model to new, unseen data to generate predictions. Once training is complete, the model’s parameters are fixed, and it processes inputs through a forward pass without updating weights. For instance, a trained image classifier might take a user-uploaded photo and output a label like “cat” or “dog.” Inference prioritizes efficiency and speed, as models are often deployed in real-time applications. Techniques like model pruning or quantization are used to reduce computational overhead during this phase. Frameworks like TensorFlow Lite or ONNX Runtime optimize models for inference on edge devices, ensuring low latency and minimal resource usage.

The key practical difference lies in their purpose and resource requirements. Training is a one-time, resource-intensive phase focused on learning, while inference is a repeated, lightweight process focused on prediction. For example, a speech recognition system might train on thousands of hours of audio data over weeks but perform inference in milliseconds per query. Developers must balance these phases: overfitting during training harms inference accuracy, while inefficient inference architectures degrade user experience. Tools like PyTorch’s torch.jit or TensorFlow Serving help bridge the gap by exporting trained models into formats optimized for deployment, ensuring that both phases align with the application’s needs.