Inference: Difference between revisions

In [[machine learning]], [[inference]] is when you make [[prediction]]s or [[generate content]] by applying a [[trained model]] to [[new data]] such as [[unlabeled examples]] or [[prompts]].

Inference in machine learning involves several steps. First, the trained model is loaded into memory and then new data is fed into it. Afterward, the model utilizes [[parameters]] and [[functions]] learned from its [[training data]] to make predictions or decisions about this new data.

In machine learning, there are two main types: [[real-time inference]] and [[batch inference]].  

#[[Batch inference]] on the other hand involves making predictions for a large [[dataset]] at once and is commonly employed when models don't need to respond in real-time like [[recommendation system]]s do.

Speed and accuracy of inference are critical factors when using machine learning models. Speed of inference is especially crucial in real-time applications since it determines the model's capability to respond rapidly to changing data. On the other hand, accuracy inference has an impact on all applications since it determines usefulness and dependability of predictions made by the model.