Serving Frameworks

Serving frameworks are essential tools in the deployment of machine learning models, enabling efficient and scalable access to these models in production environments. They facilitate the integration of models into applications, allowing for real-time predictions and data processing. These frameworks address various challenges, such as managing model versions, ensuring low-latency responses, and handling high traffic loads. By providing a structured approach to model serving, they help developers streamline the deployment process, improve performance, and maintain the reliability of AI applications. Understanding the intricacies of serving frameworks is crucial for optimizing the lifecycle of machine learning models in real-world scenarios.