Inference

Inference is the process of using a trained machine learning model to make predictions or generate outputs based on new, previously unseen input data. It's the deployment phase where a model that has already been trained is put to practical use. During training, a model learns patterns from a dataset by adjusting its internal parameters. During inference, these learned parameters remain fixed, and the model applies what it learned to new inputs. For language models, inference involves processing a prompt and generating a response token by token. For image classification models, inference means analyzing a new image and predicting its category. For recommendation systems, inference means suggesting products based on user behavior. Inference differs from training in several important ways. Training is computationally expensive and requires large amounts of data and powerful hardware. Inference is typically faster and less resource-intensive, though the exact requirements depend on model size and complexity. Training happens once or periodically to update models, while inference happens continuously as users interact with the system. Optimizing inference is crucial for practical AI applications. Techniques include model quantization (reducing numerical precision), distillation (creating smaller models that mimic larger ones), caching (storing frequently accessed results), and batching (processing multiple inputs simultaneously). The speed and cost of inference directly impact user experience and operational expenses. As AI systems scale to serve millions of users, inference optimization becomes increasingly important. Understanding inference requirements helps organizations choose appropriate hardware, estimate costs, and design systems that can serve users efficiently.