AWS sagemaker documentation change

Service: sagemaker · 2026-06-28 · Documentation low

File: sagemaker/latest/dg/mlflow-track-experiments-model-deployment.md

Summary

Updated ModelBuilder documentation link and added SageMaker Python SDK v3 deployment example for MLflow models

Security assessment

The changes update a documentation URL and provide a new SDK deployment example. The code snippet shows standard model deployment without IAM policies, encryption settings, or other security configurations. No security advisories or vulnerability fixes are present.

Diff

diff --git a/sagemaker/latest/dg/mlflow-track-experiments-model-deployment.md b/sagemaker/latest/dg/mlflow-track-experiments-model-deployment.md
index 138752200..2f729e6f4 100644
--- a//sagemaker/latest/dg/mlflow-track-experiments-model-deployment.md
+++ b//sagemaker/latest/dg/mlflow-track-experiments-model-deployment.md
@@ -13 +13 @@ You can deploy MLflow models to a SageMaker AI endpoint using Amazon SageMaker A
-`ModelBuilder` is a Python class that takes a framework model or a user-specified inference specification and converts it to a deployable model. For more details about the `ModelBuilder` class, see [ModelBuilder](https://sagemaker.readthedocs.io/en/stable/api/inference/model_builder.html#sagemaker.serve.builder.model_builder.ModelBuilder).
+`ModelBuilder` is a Python class that takes a framework model or a user-specified inference specification and converts it to a deployable model. For more details about the `ModelBuilder` class, see [ModelBuilder](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_serve.html).
@@ -69,0 +70,29 @@ Use the following code example for reference. For end-to-end examples that show
+SageMaker Python SDK v3
+    
+    
+    
+    from sagemaker.serve import ModelBuilder
+    from sagemaker.serve.mode.function_pointers import Mode
+    from sagemaker.serve import SchemaBuilder
+    
+    my_schema = SchemaBuilder(
+        sample_input=sample_input, 
+        sample_output=sample_output
+    )
+    
+    model_builder = ModelBuilder(
+        mode=Mode.SAGEMAKER_ENDPOINT,
+        schema_builder=my_schema,
+        role_arn="Your-service-role-ARN",
+        model_metadata={
+            # both model path and tracking server ARN are required if you use an mlflow run ID or mlflow model registry path as input
+            "MLFLOW_MODEL_PATH": "models:/sklearn-model/1"
+            "MLFLOW_TRACKING_ARN": "arn:aws:sagemaker:region:account-id:mlflow-tracking-server/tracking-server-name"
+        }
+    )
+    model = model_builder.build()
+    endpoint = model_builder.deploy(endpoint_name="my-endpoint", instance_type="ml.c6i.xlarge")
+
+SageMaker Python SDK v2 (Legacy)
+    
+