AWS sagemaker documentation change

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

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

Summary

Removed SageMaker Python SDK v2 (legacy) code example and MLflow implementation

Security assessment

Change deletes legacy SDK implementation without security justification. No security-related content added or modified in the documentation.

Diff

diff --git a/sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md b/sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md
index 43892447d..533bef013 100644
--- a//sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md
+++ b//sagemaker/latest/dg/mlflow-track-experiments-log-metrics.md
@@ -45,3 +44,0 @@ The following example takes you through a basic model training workflow using SK
-SageMaker Python SDK v3
-    
-    
@@ -105,26 +101,0 @@ SageMaker Python SDK v3
-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}")
-