AWS sagemaker documentation change
Summary
Restructured documentation headings and removed redundant section titles
Security assessment
Organizational change without security impact. Content about IAM/KMS remains unchanged security documentation.
Diff
diff --git a/sagemaker/latest/dg/nova-distillation.md b/sagemaker/latest/dg/nova-distillation.md index 59683c9bf..0db2c6201 100644 --- a//sagemaker/latest/dg/nova-distillation.md +++ b//sagemaker/latest/dg/nova-distillation.md @@ -5 +5 @@ -Concepts +Key componentsUse casesPrerequisitesSet up data augmentationStart a training jobCloudWatch logsSuccessful trainingValidate augmented data quality @@ -11,9 +10,0 @@ This quick start guide helps you get started with Amazon Nova model distillation -###### Topics - - * Concepts - - - - -## Concepts - @@ -22 +13 @@ Model distillation is a method that transfers knowledge from large, advanced mod -### Key components +## Key components @@ -46 +37 @@ The distillation process primarily involves two types of models: -### Use cases +## Use cases @@ -59 +50 @@ Mode distillation is particularly beneficial when: -### Prerequisites +## Prerequisites @@ -70 +61 @@ Mode distillation is particularly beneficial when: -### Setting up data augmentation +## Setting up data augmentation @@ -74 +65 @@ The data augmentation phase uses SageMaker training jobs to generate high-qualit -#### IAM role +### IAM role @@ -167 +158 @@ Attach the following inline policy to customer execution role needed for Distill -#### Amazon VPC configuration +### Amazon VPC configuration @@ -213 +204 @@ For each endpoint: -#### AWS KMS keys +### AWS KMS keys @@ -239 +230 @@ Save the KMS key ARN from the output as you'll need it for the Amazon S3 bucket -#### Amazon S3 bucket +### Amazon S3 bucket @@ -266 +257 @@ Example command to create an Amazon S3 bucket with AWS KMS encryption. Replace ` -### Starting a SageMaker training job +## Starting a SageMaker training job @@ -410 +401 @@ The following sample notebook demonstrates how to run a SageMaker training job f -### CloudWatch logs +## CloudWatch logs @@ -414 +405 @@ Logs are available in Amazon CloudWatch under the `/aws/sagemaker/TrainingJobs` -### Successful training +## Successful training @@ -440 +431 @@ The output bucket contains the following files: -### Validating augmented data quality +## Validating augmented data quality