• AWS News Blog New serverless customization in Amazon SageMaker AI accelerates model fine-tuning | Today, I’m happy to announce new serverless customization in Amazon SageMaker AI for popular AI models, such as Amazon Nova, DeepSeek, GPT-OSS, Llama, and Qwen. • The new customization capability provides an easy-to-use interface for the latest fine-tuning techniques like reinforcement learning, so you can accelerate the AI model customization process from months to days. • With a few clicks, you can seamlessly select a model and customization technique, and handle model evaluation and deployment-all entirely serverless so you can focus on model tuning rather than managing infrastructure. • When you choose serverless customization, SageMaker AI automatically selects and provisions the appropriate compute resources based on the model and data size. • Getting started with serverless model customization You can get started customizing models in Amazon SageMaker Studio. • Choose Models in the left navigation pane and check out your favorite AI models to be customized.
Article Summaries:
- AWS announced a new serverless customization feature in Amazon SageMaker AI that lets users fine‑tune popular models-including Amazon Nova, DeepSeek, GPT‑OSS, Llama, and Qwen-directly from SageMaker Studio. The interface supports supervised fine‑tuning and advanced reinforcement‑learning techniques such as Direct Preference Optimization, RLVR, and RLAIF, automatically provisioning compute resources based on model and data size. After training, users can evaluate, iterate, and deploy models to either SageMaker inference endpoints or Amazon Bedrock for serverless inference. The update also introduces a serverless MLflow application for experiment tracking, streamlining the entire model‑customization workflow.
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