AWS nova documentation change
Summary
Updated title and terminology from 'SageMaker AI training jobs' to 'SageMaker Training Jobs', fixed a relative link to recipes documentation, and consolidated training method references by replacing two specific fine-tuning links with a general training page link.
Security assessment
Changes involve terminology updates, link fixes, and content reorganization. No security vulnerabilities, incidents, or encryption changes are mentioned. The existing security note about S3 encryption remains unchanged.
Diff
diff --git a/nova/latest/nova2-userguide/nova-model-training-job.md b/nova/latest/nova2-userguide/nova-model-training-job.md index e4ae0fcb5..02d4f9804 100644 --- a//nova/latest/nova2-userguide/nova-model-training-job.md +++ b//nova/latest/nova2-userguide/nova-model-training-job.md @@ -7 +7 @@ Best Practices for Amazon Nova customization -# Amazon Nova customization on SageMaker AI training jobs +# Amazon Nova customization on SageMaker Training Jobs @@ -9 +9 @@ Best Practices for Amazon Nova customization -SageMaker AI training jobs is an environment that enables you to train machine learning models at scale. It automatically provisions and scales compute resources, loads training data from sources like Amazon S3, executes your training code, and stores the resulting model artifacts. +SageMaker Training Jobs is an environment that enables you to train machine learning models at scale. It automatically provisions and scales compute resources, loads training data from sources like Amazon S3, executes your training code, and stores the resulting model artifacts. @@ -11 +11 @@ SageMaker AI training jobs is an environment that enables you to train machine l -The purpose of training is to customize the base Amazon Nova model using your proprietary data. The training process typically involves steps to prepare your data, choose a [recipe](https://docs.aws.amazon.com/sagemaker/latest/dg/nova-model-recipes.html), modify configuration parameters in YAML files, and submit a training job. The training process will output trained model checkpoint in a service-managed Amazon S3 bucket. You can use this checkpoint location for evaluation jobs. Nova customization on SageMaker AI training jobs stores model artifacts in a service-managed Amazon S3 bucket. Artifacts in the service-managed bucket are encrypted with SageMaker AI-managed KMS keys. Service-managed Amazon S3 buckets don't currently support data encryption using customer-managed KMS keys. +The purpose of training is to customize the base Amazon Nova model using your proprietary data. The training process typically involves steps to prepare your data, choose a [recipe](./nova-model-recipes.html), modify configuration parameters in YAML files, and submit a training job. The training process will output trained model checkpoint in a service-managed Amazon S3 bucket. You can use this checkpoint location for evaluation jobs. Nova customization on SageMaker AI training jobs stores model artifacts in a service-managed Amazon S3 bucket. Artifacts in the service-managed bucket are encrypted with SageMaker AI-managed KMS keys. Service-managed Amazon S3 buckets don't currently support data encryption using customer-managed KMS keys. @@ -112,3 +112 @@ Don't use SFT when the gap is knowledge rather than behavior. It doesn't teach t - * [Fine-tune Nova 2.0](./nova-fine-tune-2.html) - - * [Reinforcement Fine-Tuning (RFT) with Amazon Nova models](./nova-reinforcement-fine-tuning.html) + * [Training for Amazon Nova models](./smtj-training.html)