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Building Scalable AI Systems
This chapter explores the intricate process of constructing large-scale AI systems, focusing on optimal sharding and the challenges of global mini-batch constraints. It emphasizes the necessity for error recovery, the role of AWS services in facilitating efficient machine learning training, and the implementation of sparsity techniques. Furthermore, it discusses advancements in the Trainium architecture, highlighting performance optimization through innovative designs and collaborations with key customers.