Operationalizing inference strategies in LLMOps

Inference in the context of LLMs refers to the process of applying a trained model to new data to make predictions or generate text. This stage is critical in the life cycle of a machine learning (ML) model because it’s when the model delivers its intended value, serving requests from end users or other systems. Unlike the training phase, which is a one-time process (albeit one that is possibly iterated upon), inference happens continuously as users interact with applications powered by LLMs. The efficiency, reliability, and scalability of the inference process directly impact the user experience, making it a cornerstone of LLMOps.

Decoding inference types – real-time, batch, and interactive

Real-time inference is needed in applications requiring immediate responses, such as chatbots or real-time content recommendations. The key metrics here are latency and throughput, as the system must handle individual requests...