AWS launches SageMaker Training Sessions for custom AI model training

Amazon Web Services wants developers to stop babysitting GPUs. On October 22, 2026, the company announced general availability of Amazon SageMaker AI Training Sessions, a serverless feature for customizing AI models. The pitch is simple. Developers write the training logic, and AWS manages the hardware, the scaling and the distributed plumbing underneath. What Training Sessions actually does In technical terms, Training Sessions lets developers run their own post-training loops. Supported approaches include supervised fine-tuning (SFT), reinforcement learning (RL), custom loss functions and multi-turn RL. For the uninitiated, supervised fine-tuning means showing a model examples of correct answers. Reinforcement learning means rewarding it when it gets things right, so it learns which behaviors pay off. Multi-turn RL extends that reward process across longer, back-and-forth interactions, which is closer to how agents operate in practice. Meanwhile, AWS handles GPU management, distributed execution and infrastructure scaling. The developer keeps algorithmic control without inheriting the operations headache. Access runs through Python SDK clients, either from a local machine or from...