AWS sagemaker documentation change

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

File: sagemaker/latest/dg/inference-pipeline-real-time.md

Summary

Removed legacy SageMaker Python SDK v2 code examples for pipeline model deployment and real-time prediction calls.

Security assessment

Documentation update removing deprecated implementation examples. No security-related content added or modified.

Diff

diff --git a/sagemaker/latest/dg/inference-pipeline-real-time.md b/sagemaker/latest/dg/inference-pipeline-real-time.md
index d73136108..c15e62921 100644
--- a//sagemaker/latest/dg/inference-pipeline-real-time.md
+++ b//sagemaker/latest/dg/inference-pipeline-real-time.md
@@ -30,3 +29,0 @@ The following code creates and deploys a real-time inference pipeline model with
-SageMaker Python SDK v3
-    
-    
@@ -53,17 +49,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    from sagemaker.model import Model
-    from sagemaker.pipeline_model import PipelineModel
-    from sagemaker.sparkml.model import SparkMLModel
-    
-    sparkml_data = 's3://{}/{}/{}'.format(s3_model_bucket, s3_model_key_prefix, 'model.tar.gz')
-    sparkml_model = SparkMLModel(model_data=sparkml_data)
-    xgb_model = Model(model_data=xgb_model.model_data, image=training_image)
-    
-    model_name = 'serial-inference-' + timestamp_prefix
-    endpoint_name = 'serial-inference-ep-' + timestamp_prefix
-    sm_model = PipelineModel(name=model_name, role=role, models=[sparkml_model, xgb_model])
-    sm_model.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge', endpoint_name=endpoint_name)
-
@@ -74,3 +53,0 @@ The following example shows how to make real-time predictions by calling an infe
-SageMaker Python SDK v3
-    
-    
@@ -126,52 +102,0 @@ SageMaker Python SDK v3
-SageMaker Python SDK v2 (Legacy)
-    
-    
-    
-    import sagemaker
-    from sagemaker.predictor import json_serializer, json_deserializer, Predictor
-    
-    payload = {
-            "input": [
-                {
-                    "name": "Pclass",
-                    "type": "float",
-                    "val": "1.0"
-                },
-                {
-                    "name": "Embarked",
-                    "type": "string",
-                    "val": "Q"
-                },
-                {
-                    "name": "Age",
-                    "type": "double",
-                    "val": "48.0"
-                },
-                {
-                    "name": "Fare",
-                    "type": "double",
-                    "val": "100.67"
-                },
-                {
-                    "name": "SibSp",
-                    "type": "double",
-                    "val": "1.0"
-                },
-                {
-                    "name": "Sex",
-                    "type": "string",
-                    "val": "male"
-                }
-            ],
-            "output": {
-                "name": "features",
-                "type": "double",
-                "struct": "vector"
-            }
-        }
-    
-    predictor = Predictor(endpoint=endpoint_name, sagemaker_session=sagemaker.Session(), serializer=json_serializer,
-                                    content_type='text/csv', accept='application/json')
-    
-    print(predictor.predict(payload))
-