AWS Opens SageMaker Training Sessions for Custom AI Model Workflows

Amazon Web Services (AWS) made SageMaker AI Training Sessions generally available Thursday, Oct. 22, giving developers a way to write custom model-training routines while SageMaker manages the GPU operations. The Python interface exposes sample(), forward_backward() and optim_step(). Developers can combine these operations for supervised fine-tuning, reinforcement learning or a training objective of their own. SageMaker assigns GPU capacity, coordinates distributed jobs, recovers interrupted work and saves checkpoints. AWS also released a cookbook with ready-to-run examples for supervised fine-tuning and reinforcement learning. Four base models are supported at launch: gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B and Qwen3.8-27B.

Source: TokenPost