An Implementation on Building Advanced Multi-Endpoint Machine Learning APIs with LitServe: Batching, Streaming, Caching, and Local Inference

LitServe emerges as a lightweight yet robust framework for deploying machine learning models as APIs, enabling developers to create scalable, multi-endpoint serving solutions with minimal effort. The framework supports advanced functionalities such as batching, streaming, multi-task processing, and caching, all of which can be implemented and tested locally without reliance on external APIs, thereby streamlining the development of production-ready ML pipelines. By leveraging LitServe alongside popular libraries like PyTorch and Transformers, developers can efficiently set up, serve, and extend complex ML models, exemplified through use cases like text generation with models such as DistilGPT-2