SageMaker AI adds customizable serverless training sessions
- SageMaker AI serverless model customization training sessions are generally available, with teams writing their own post-training loops while AWS manages GPU allocation, distributed execution, recovery, and checkpoint storage.
- The sessions support SFT, DPO, RLVR, RLAIF, and multi-turn RL; teams can choose LoRA or full fine-tuning and combine techniques.
- User code runs the training loop and reward function in its own environment, while the session’s sampler generates responses and its trainer computes gradients and updates model weights.
- Billing is based on tokens processed by SageMaker AI; charges pause while the reward function scores a batch in the user’s environment, and capacity does not need to be reserved.
Our take
This sits between SageMaker AI’s managed multi-turn RL option and self-managed training: teams can define reward and update logic without operating GPU clusters. The article describes token-based billing but gives no rates or comparison with self-managed GPU costs, so the budget impact remains unknown.
Sources
- Announcing new ways to customize models with SageMaker AI, without managing GPUs · AWS Machine Learning
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