Amazon SageMaker AI now supports serverless model customization for Gemma 4 models

Amazon SageMaker AI Updates

Amazon SageMaker AI now supports serverless model customization for Gemma 4 E4B and 31B models using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT). This launch extends the variety of models available for serverless customization on SageMaker AI, including models from the Nova, Nemotron 3, Qwen, Llama, gpt-oss, and DeepSeek families.

Model customization enables you to tailor these foundation models with your proprietary data, improving accuracy on domain-specific tasks, aligning outputs with your organization's tone, or enhancing performance on new tasks using your labeled data. SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation.

What to do

• Navigate to the Models page in Amazon SageMaker Studio to launch a customization job.
• Use the SageMaker Python SDK for programmatic access.
• Refer to the Amazon SageMaker AI model customization documentation for more information.

Source: AWS release notes

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