Serving frameworks are specialized tools designed to deploy machine learning models, particularly large language models (LLMs), in a production environment. They facilitate the process of making these models accessible for inference, allowing applications to utilize their capabilities effectively. By providing a structured approach to model serving, these frameworks help manage resources, optimize performance, and ensure scalability. Popular serving frameworks include vLLM, Text Generation Inference, and Ray Serve, each offering unique features tailored to specific use cases. Understanding these frameworks is essential for developers and data scientists aiming to implement efficient and reliable machine learning solutions.