Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs
AWS SageMaker now lets developers write custom training loops without managing GPU infrastructure
“You write the training loop, and SageMaker AI runs the infrastructure.”
Amazon SageMaker AI is launching training session APIs that decouple algorithmic control from infrastructure management, letting developers write custom training loops (SFT, RL, preference optimization) via simple API calls like forward_backward() and optim_step() against a live GPU-backed session. This matters because it removes a longstanding tradeoff in LLM fine-tuning between flexibility and operational overhead. It targets ML practitioners who need custom algorithms but don't want to manage distributed training infrastructure.