AWS nova documentation change
Summary
Added version notice for Amazon Nova 2, updated terminology from 'SageMaker AI' to 'SageMaker', and expanded inference options in related topics
Security assessment
Changes are administrative updates (branding/terminology adjustments), version notifications, and added documentation links. No security vulnerabilities, patches, or security-specific configurations are mentioned. The KMS encryption reference remains unchanged with no security enhancements.
Diff
diff --git a/nova/latest/userguide/nova-model.md b/nova/latest/userguide/nova-model.md index 8af589634..b42a52c03 100644 --- a//nova/latest/userguide/nova-model.md +++ b//nova/latest/userguide/nova-model.md @@ -7 +7,5 @@ -You can customize [Amazon Nova models](https://docs.aws.amazon.com//nova/latest/userguide/what-is-nova.html), including the enhanced Amazon Nova 2.0 models, through [recipes](./nova-model-recipes.html#nova-model-get-recipes) and train them on SageMaker AI. These recipes support techniques such as supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT), with both full-rank and low-rank adaptation (LoRA) options. +###### Note + +This documentation is for Amazon Nova Version 1. Amazon Nova 2 is now available with new models and enhanced capabilities. For information on how to customize Amazon Nova 2, visit [Customizing Amazon Nova 2 models](https://docs.aws.amazon.com/nova/latest/nova2-userguide/nova-model.html). + +You can customize [Amazon Nova models](https://docs.aws.amazon.com//nova/latest/userguide/what-is-nova.html), including the enhanced Amazon Nova 2.0 models, through [recipes](./nova-model-recipes.html#nova-model-get-recipes) and train them on SageMaker. These recipes support techniques such as supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT), with both full-rank and low-rank adaptation (LoRA) options. @@ -9 +13 @@ You can customize [Amazon Nova models](https://docs.aws.amazon.com//nova/latest/ -The end-to-end customization workflow involves stages like model training, model evaluation, and deployment for inference. This model customization approach on SageMaker AI provides greater flexibility and control to fine-tune its supported Amazon Nova models, optimize hyperparameters with precision, and implement techniques such as LoRA parameter-efficient fine-tuning (PEFT), full-rank SFT, DPO, RFT, Continued Pre-Training (CPT), Proximal Policy Optimization (PPO), etc. +The end-to-end customization workflow involves stages like model training, model evaluation, and deployment for inference. This model customization approach on SageMaker provides greater flexibility and control to fine-tune its supported Amazon Nova models, optimize hyperparameters with precision, and implement techniques such as LoRA parameter-efficient fine-tuning (PEFT), full-rank SFT, DPO, RFT, Continued Pre-Training (CPT), Proximal Policy Optimization (PPO), etc. @@ -11 +15 @@ The end-to-end customization workflow involves stages like model training, model -SageMaker AI offers two environments for customizing Amazon Nova models. +SageMaker offers two environments for customizing Amazon Nova models. @@ -13 +17 @@ SageMaker AI offers two environments for customizing Amazon Nova models. - * [**SageMaker AI training jobs**](https://docs.aws.amazon.com//sagemaker/latest/dg/how-it-works-training.html) provides a fully managed environment for customizing Amazon Nova models where you don't need to create or maintain any clusters. The service automatically handles all infrastructure provisioning, scaling, and resource management, allowing you to focus solely on configuring your training parameters and submitting your job. You can customize Nova models on SageMaker AI training jobs with techniques like Parameter Efficient Fine-tuning (PEFT), Full rank fine tuning, Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). For more information, see [Amazon Nova customization on SageMaker AI training jobs](./nova-model-training-job.html). + * [**SageMaker training jobs**](https://docs.aws.amazon.com//sagemaker/latest/dg/how-it-works-training.html) provides a fully managed environment for customizing Amazon Nova models where you don't need to create or maintain any clusters. The service automatically handles all infrastructure provisioning, scaling, and resource management, allowing you to focus solely on configuring your training parameters and submitting your job. You can customize Nova models on SageMaker training jobs with techniques like Parameter Efficient Fine-tuning (PEFT), Full rank fine tuning, Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). For more information, see [Amazon Nova customization on SageMaker Training Jobs](./nova-model-training-job.html). @@ -23 +27 @@ If you provide a KMS key to your Amazon Nova model customization training job fo - * [**SageMaker AI HyperPod**](https://docs.aws.amazon.com//sagemaker/latest/dg/sagemaker-hyperpod.html) offers a specialized environment to train Amazon Nova models by requiring you to create and manage EKS clusters with restricted instance groups (RIGs). This environment gives you flexibility in configuring your training environment with specialized GPU instances and integrated Amazon FSx for Lustre storage, making it particularly well-suited for advanced distributed training scenarios and ongoing model development. For more information, see [Amazon Nova customization on SageMaker AI Hyperpod ](./nova-hp.html). + * [**SageMaker HyperPod**](https://docs.aws.amazon.com//sagemaker/latest/dg/sagemaker-hyperpod.html) offers a specialized environment to train Amazon Nova models by requiring you to create and manage EKS clusters with restricted instance groups (RIGs). This environment gives you flexibility in configuring your training environment with specialized GPU instances and integrated Amazon FSx for Lustre storage, making it particularly well-suited for advanced distributed training scenarios and ongoing model development. For more information, see [Amazon Nova customization on SageMaker HyperPod ](./nova-hp.html). @@ -34 +38 @@ If you provide a KMS key to your Amazon Nova model customization training job fo - * [Amazon Nova customization on SageMaker AI training jobs](./nova-model-training-job.html) + * [Amazon Nova customization on SageMaker Training Jobs](./nova-model-training-job.html) @@ -36 +40 @@ If you provide a KMS key to your Amazon Nova model customization training job fo - * [Amazon Nova customization on SageMaker AI Hyperpod ](./nova-hp.html) + * [Amazon Nova customization on SageMaker HyperPod ](./nova-hp.html) @@ -39,0 +44,4 @@ If you provide a KMS key to your Amazon Nova model customization training job fo + * [SageMaker Inference](./nova-model-sagemaker-inference.html) + + * [Amazon Bedrock inference](./nova-model-bedrock-inference.html) +