AI Inference
AI Inference is the process of using a trained machine learning model to make predictions, classifications, or generate outputs on new, unseen data. This phase occurs after model training is complete and involves feeding input data through the trained neural network to produce results. AI inference encompasses various computational processes including forward propagation, attention mechanisms, and output generation, depending on the model architecture. Key considerations include latency optimization, throughput maximization, memory efficiency, and hardware acceleration using GPUs, TPUs, or specialized inference chips. Inference can be performed in real-time for interactive applications, batch processing for large datasets, or edge deployment for local processing. Modern inference systems employ techniques like model quantization, pruning, distillation, and caching to optimize performance and reduce computational costs. AI inference is critical for production AI applications including recommendation systems, computer vision, natural language processing, and autonomous systems where trained models must deliver accurate, fast responses to user queries or environmental inputs.