AWS sagemaker documentation change

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

File: sagemaker/latest/dg/inference-pipeline-batch.md

Summary

Removed legacy SageMaker Python SDK v2 code example for batch transform job configuration.

Security assessment

Routine removal of outdated code samples. No security implications or vulnerability fixes mentioned.

Diff

diff --git a/sagemaker/latest/dg/inference-pipeline-batch.md b/sagemaker/latest/dg/inference-pipeline-batch.md
index 1044ee8c1..a6d2f5fd8 100644
--- a//sagemaker/latest/dg/inference-pipeline-batch.md
+++ b//sagemaker/latest/dg/inference-pipeline-batch.md
@@ -17,3 +16,0 @@ The following example shows how to run a transform job using the [Amazon SageMak
-SageMaker Python SDK v3
-    
-    
@@ -50,21 +46,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import sagemaker
-    input_data_path = 's3://{}/{}/{}'.format(default_bucket, 'key', 'file_name')
-    output_data_path = 's3://{}/{}'.format(default_bucket, 'key')
-    transform_job = sagemaker.transformer.Transformer(
-        model_name = model_name,
-        instance_count = 1,
-        instance_type = 'ml.m4.xlarge',
-        strategy = 'SingleRecord',
-        assemble_with = 'Line',
-        output_path = output_data_path,
-        base_transform_job_name='inference-pipelines-batch',
-        sagemaker_session=sagemaker.Session(),
-        accept = CONTENT_TYPE_CSV)
-    transform_job.transform(data = input_data_path, 
-                            content_type = CONTENT_TYPE_CSV, 
-                            split_type = 'Line')
-