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ML workloads are notoriously resource-heavy, often requiring significant GPU power for training. Kubernetes allows teams to abstract this hardware, spinning up powerful clusters only when needed and scaling down once training is complete. This "pay-as-you-go" efficiency is vital for modern data science teams. 2. Portability with Containers Would you like a summary of the book's key topics instead