# Amazon — Announcing new ways to customize models with SageMaker AI, without managing GPUs

- Company: Amazon (amazon.com)
- Announced: 2026-10-22T18:00:04+00:00
- Category: capability-change
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://aws.amazon.com/blogs/machine-learning/announcing-new-ways-to-customize-models-with-sagemaker-ai-without-managing-gpus/
- Record: https://forck.live/items/26793-announcing-new-ways-to-customize-models-with-sagemaker-ai-without-managing-gpus
- Subject: Bedrock / Nova
- Models affected: gpt-oss-20b
- Pricing: You pay for the tokens SageMaker AI processes

Amazon made training sessions generally available in SageMaker AI serverless model customization, letting teams write their own post-training loop while AWS handles GPU allocation, distributed execution, fault recovery and checkpoint storage. The company says companies getting real results from AI customize open-weight models with techniques such as reinforcement learning rather than using foundation models off the shelf, but that customization requires picking a technique and managing GPU infrastructure. Training sessions let customers define custom loss functions, reward signals and RL stages in one loop, and support SFT, DPO, RLVR, RLAIF, multi-turn RL, LoRA or full fine-tuning, chained through continuous customization. A cookbook with examples and model customization agent skills works in SageMaker Studio, Kiro, Claude Code, Codex or GitHub Copilot. ImagineSoftware's Rob Gontarek said the low-level API surface and direct control over forward and backward passes, sampling and the optimizer step gave scientists the control needed for a healthcare claim-denial triage model. Karini AI's Nitin Wagh said the platform handled infrastructure while fine-tuning gpt-oss-20b for multi-turn tool calling against its Amazon Neptune knowledge graph, with reliable checkpoint-and-resume. Customers pay for tokens processed, the meter stops while a reward function scores a batch, and there is no capacity to reserve; each session uses an IAM role, with optional KMS key and VPC configuration, and data and checkpoints stay in the customer's S3 buckets.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/announcing-new-ways-to-customize-models-with-sagemaker-ai-without-managing-gpus/:

> 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.

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