diff --git a/docs/model_customization/model_customization.rst b/docs/model_customization/model_customization.rst
index 3a36a3ff28..938349bdd7 100644
--- a/docs/model_customization/model_customization.rst
+++ b/docs/model_customization/model_customization.rst
@@ -53,6 +53,116 @@ Key Features
   with clear precedence. Use ``get_resolved_recipe()`` to inspect the merged configuration
   before job submission. See :doc:`finetuning_serverful` and :doc:`finetuning_hyperpod` for examples.
 
+**Job Notifications**
+  Receive SNS notifications when training jobs complete, fail, or stop. Uses EventBridge
+  rules to route SageMaker Training Job status changes to your SNS topic. 
+
+  The SDK creates one rule per unique config (topic + events + prefix). Re-running with 
+  the same config reuses the existing rule. Different configs create separate rules.
+
+  .. note::
+    Supported for SMTJ (serverful and serverless) compute only. HyperPod is not currently supported.
+
+  .. code-block:: python
+
+    trainer = SFTTrainer(
+        model="nova-textgeneration-micro",
+        training_dataset="s3://my-bucket/train.jsonl",
+        accept_eula=True,
+        notifications={
+            "sns_topic_arn": "arn:aws:sns:us-east-1:123456789012:my-topic",  # Required
+            "events": ["Completed", "Failed"],    # Optional (default: Completed, Failed, Stopped)
+            "job_name_prefix": "my-team-sft-",    # Optional: filter by job name
+            "event_bus_arn": "arn:aws:events:us-east-1:123456789012:event-bus/custom-bus" # Optional
+        },
+    )
+    job = trainer.train(wait=False)  # Notification sent on completion
+
+    # Access the rule ARN
+    print(trainer.notification_rule_arn)
+
+    # List and manage rules
+    rules = trainer.list_notification_rules()
+    trainer.delete_notification_rule(rule_arn=trainer.notification_rule_arn)
+
+  **Prerequisites:**
+
+  - An SNS topic (and subscription) with a resource policy allowing ``events.amazonaws.com``. 
+    To set up a topic and subscription, see `Creating an SNS topic and subscription <https://docs.aws.amazon.com/sns/latest/dg/sns-create-subscribe-endpoint-to-topic.html>`_. 
+  - IAM permissions: ``events:PutRule``, ``events:PutTargets``, ``events:ListRules``,
+    ``events:RemoveTargets``, ``events:DeleteRule``
+
+**Monitoring: show_metrics()**
+  Plot training metrics after a job completes. Works across all compute types.
+
+  - **Nova models**: Metrics parsed from CloudWatch logs.
+  - **OSS models**: Metrics pulled from MLflow.
+
+  .. code-block:: python
+
+    # Plot all available metrics
+    df = trainer.show_metrics()
+
+    # Plot specific metrics
+    df = trainer.show_metrics(metrics=["training_loss", "lr"])
+
+    # Filter by step range
+    df = trainer.show_metrics(starting_step=10, ending_step=100)
+
+    # Filter by time window
+    from datetime import datetime
+    df = trainer.show_metrics(
+        start_time=datetime(2026, 1, 1, 10, 0, 0),
+        end_time=datetime(2026, 1, 1, 12, 0, 0),
+    )
+
+  **After a kernel restart:**
+
+  .. code-block:: python
+
+    # Standalone (SMTJ)
+    from sagemaker.train import plot_training_metrics
+    plot_training_metrics("my-sft-job")
+
+    # Re-attach (HyperPod — needs cluster name for log group resolution)
+    from sagemaker.train import SFTTrainer
+    from sagemaker.core.training.configs import HyperPodCompute
+
+    trainer = SFTTrainer(
+        model="nova-textgeneration-micro",
+        training_dataset="s3://unused",
+        compute=HyperPodCompute(cluster_name="my-cluster", instance_type="ml.p5.48xlarge")
+    )
+    trainer._latest_training_job = "my-hp-job"
+    df = trainer.show_metrics()
+
+**Monitoring: stream_logs()**
+  Stream CloudWatch logs in real-time while a job is running.
+
+  .. code-block:: python
+
+    # Start training non-blocking
+    job = trainer.train(wait=False)
+
+    # Stream logs (blocks until job completes or Ctrl+C)
+    trainer.stream_logs()
+
+    # Custom polling interval (seconds)
+    trainer.stream_logs(poll=10)
+
+    # Stream from a specific start time - providing this will speed up execution.
+    from datetime import datetime
+    trainer.stream_logs(start_time=datetime(2026, 1, 1, 15, 0, 0))
+
+  .. note::
+
+    - **SMTJ**: Streaming auto-stops when the job reaches a terminal state.
+    - **HyperPod**: Streaming runs until you press Ctrl+C. Logs may take a few minutes
+      to propagate to CloudWatch on first run.
+
+
+----
+
 
 .. toctree::
    :maxdepth: 1