Today, Amazon Nova is introducing the most comprehensive suite of model customization capabilities made available for any proprietary model family. Available as ready-to-use recipes on SageMaker AI, these capabilities allow customers to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment.
Using these customization techniques, you can adapt Nova models to accurately reflect your proprietary knowledge, workflows, and brand in your generative AI applications while maintaining Nova’s industry-leading price performance and low latency. The techniques include Continued Pre-Training, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Proximal Policy Optimization, and Knowledge Distillation — with support for both parameter-efficient and full-model training options across SFT, DPO and Distillation.
Nova customization recipes are available in SageMaker training jobs and SageMaker HyperPod, giving you flexibility to select the environment that best fits your infrastructure and scale requirements. You can deploy your customized models on Amazon Bedrock and invoke them via on-demand inference or Provisioned Throughput. On-demand inference is available only with parameter efficient training techniques.
Recipes for Amazon Nova on Amazon SageMaker AI are available in US East (N. Virginia).
To get started read Amazon Nova user guide and visit the GitHub repository to browse Nova specific SageMaker training recipes.
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Source: Amazon Web Services
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