Optimizing Hybrid Cloud for Machine Learning

This chapter explores hybrid cloud computing by integrating Infrastructure as a Service (IaaS) and Function as a Service (FaaS) to enhance cost efficiency and scalability for machine learning applications. It discusses challenges related to model deployment, resource management techniques like spot instances, and the intricacies of operator parallelism and batching in improving processing speeds. The conversation emphasizes the importance of continuous cost management and the complexities of configuring parameters for real-time inference systems.