Announcing new ways to customize models with SageMaker AI, without managing GPUs

Amazon SageMaker AI has rolled out serverless model customization with fully available training sessions, letting developers write and orchestrate their own post‑training loops—such as custom loss functions, reward signals, or multi‑turn reinforcement learning—while SageMaker AI automatically provisions GPU resources and handles distributed execution. This eliminates the need to manage GPU clusters, streamlining the workflow for adding fine‑tuning logic to large language models or other deep learning workloads. The new feature supports a range of custom training patterns, but users must still configure the training script and specify the desired compute settings through SageMaker AI’s API. The trade‑off is that while operational overhead drops, users give up direct control over GPU allocation granularity and may face higher per‑

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