AWS sagemaker documentation change

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

File: sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md

Summary

Added SageMaker Python SDK v2 code example for MLflow metric logging and model registration

Security assessment

The change adds a legacy SDK implementation example for MLflow workflows. It demonstrates standard metric logging and model registration without any security configurations, vulnerability mitigations, or security feature explanations.

Diff

diff --git a/sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md b/sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md
index 533bef013..43892447d 100644
--- a//sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md
+++ b//sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md
@@ -44,0 +45,3 @@ The following example takes you through a basic model training workflow using SK
+SageMaker Python SDK v3
+    
+    
@@ -101,0 +105,26 @@ The following example takes you through a basic model training workflow using SK
+SageMaker Python SDK v2 (Legacy)
+    
+    
+    
+    import mlflow.sklearn
+    from mlflow.models import infer_signature
+    from sklearn.datasets import make_regression
+    from sklearn.ensemble import RandomForestRegressor
+    
+    mlflow.set_tracking_uri(arn)
+    params = {"n_estimators": 3, "random_state": 42}
+    X, y = make_regression(n_features=4, n_informative=2, random_state=0, shuffle=False)
+    
+    # Log MLflow entities
+    with mlflow.start_run() as run:
+        rfr = RandomForestRegressor(**params).fit(X, y)
+        signature = infer_signature(X, rfr.predict(X))
+        mlflow.log_params(params)
+        mlflow.sklearn.log_model(rfr, artifact_path="sklearn-model", signature=signature)
+    
+    model_uri = f"runs:/{run.info.run_id}/sklearn-model"
+    mv = mlflow.register_model(model_uri, "RandomForestRegressionModel")
+    
+    print(f"Name: {mv.name}")
+    print(f"Version: {mv.version}")
+