👉 Detector computing, also known as inference or prediction computing, is a computational process that follows the training phase of machine learning models to generate predictions or outputs based on input data. After a model is trained on a large dataset, it enters the inference phase where it processes new, unseen data to make predictions or classify inputs. This involves running the trained model through the input data, which can be images, text, audio, or other types of data, to produce a corresponding output. Detector computing is essential for deploying machine learning models in real-world applications, such as image recognition, natural language processing, and autonomous systems, where the model must make quick and accurate decisions based on incoming data. The efficiency and accuracy of detector computing depend on the model's architecture, the complexity of the input data, and the computational resources available, including hardware like GPUs or TPUs.
