AWS sagemaker documentation change

Service: sagemaker · 2026-07-01 · Documentation low

File: sagemaker/latest/dg/use-scikit-learn-processing-container.md

Summary

Removed legacy SageMaker Python SDK v2 code examples and SDK version headers

Security assessment

The changes remove outdated code samples without introducing security-related content or addressing vulnerabilities. No security implications identified.

Diff

diff --git a/sagemaker/latest/dg/use-scikit-learn-processing-container.md b/sagemaker/latest/dg/use-scikit-learn-processing-container.md
index 72ad1b448..2b66d7a92 100644
--- a//sagemaker/latest/dg/use-scikit-learn-processing-container.md
+++ b//sagemaker/latest/dg/use-scikit-learn-processing-container.md
@@ -19,3 +18,0 @@ The following code example shows how to run a processing job using a scikit-lear
-SageMaker Python SDK v3
-    
-    
@@ -69,22 +65,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.sklearn.processing import SKLearnProcessor
-    from sagemaker.processing import ProcessingInput, ProcessingOutput
-    
-    sklearn_processor = SKLearnProcessor(framework_version='0.20.0',
-                                         role=role,
-                                         instance_type='ml.m5.xlarge',
-                                         instance_count=1)
-    
-    sklearn_processor.run(code='preprocessing.py',
-                          inputs=[ProcessingInput(
-                            source='s3://path/to/my/input-data.csv',
-                            destination='/opt/ml/processing/input')],
-                          outputs=[ProcessingOutput(source='/opt/ml/processing/output/train'),
-                                   ProcessingOutput(source='/opt/ml/processing/output/validation'),
-                                   ProcessingOutput(source='/opt/ml/processing/output/test')]
-                         )
-    
-