Navigating Machine Learning Deployment Challenges

This chapter explores the journey from simple machine learning models to complex deep learning deployments in large enterprises, focusing on the operational challenges faced by companies like Uber. It discusses the transition from CPU to GPU computing, distributed training complexities, and the need for streamlined data workflows in production environments. The chapter emphasizes the importance of integrating traditional data processing with AI, addressing issues related to model performance, management, and the evolving demands of generative AI applications.