Optimizing Workload Management in AI Infrastructure

This chapter focuses on the Dynamic Workload Scheduler (DWS) designed to optimize resource availability for high-demand components such as GPUs while addressing challenges around hardware access. It explores the management of a multi-tenant machine learning platform operating on Kubernetes, emphasizing fairness and transparency in resource sharing. Additionally, the chapter discusses the evolution of machine learning technologies, best practices for infrastructure teams, and the balance needed between rapid innovation and platform stability.