Companies getting real results from AI aren’t only using foundation models (FMs) off the shelf. They’re customizing open-weight models using post-training techniques such as reinforcement learning. A model trained on your business context, product catalog, or clinical vocabulary will be more accurate for your task and cost less to run. When you build agents, your data, workflows, evaluations, and success criteria also shape how the model behaves.

However, model customization is difficult. You must choose the right technique, set up and manage GPU infrastructure, and, for agents, build environments for multi-turn reinforcement learning. We launched Amazon SageMaker AI serverless model customization to solve these challenges, and today, we’re expanding it with the general availability launch of training sessions.

With training sessions you get control over the post-training logic without managing the underlying infrastructure. You write and coordinate the training loop, including custom loss functions or reward signals. SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage.

With SageMaker AI serverless model customization, you don’t choose instance types, manage clusters, or write distributed training configuration.

We didn’t want to limit you. You can use techniques such as SFT, DPO, RLVR, RLAIF, or multi-turn RL. You can choose how much of the model to update with Low-Rank Adaptation (LoRA) or full fine-tuning and chain techniques together through continuous customization.

We didn’t want you to waste time . Our SageMaker AI training sessions cookbook provides examples and use cases, along with model customization agent skills. You describe your use case, and the agent handles data prep, technique choice, training, evaluation, and deployment.

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