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Navigating Machine Learning Infrastructure
This chapter explores the distinct infrastructure demands for training versus inference in machine learning, highlighting the resource intensity of training and the efficiency required for inference. It discusses the challenges of compute provisioning and the importance of auto-scaling, along with the financial implications of over-provisioning in the AI industry. Using analogies and anecdotal evidence, the chapter emphasizes the need for scalable solutions and addresses concerns over outdated compute resources amid shifting market behaviors.