diff --git a/README.rst b/README.rst
index 8f0a913b82..1a6e3dfd40 100644
--- a/README.rst
+++ b/README.rst
@@ -30,7 +30,8 @@ Table of Contents
 5. `Chainer SageMaker Estimators <#chainer-sagemaker-estimators>`__
 6. `AWS SageMaker Estimators <#aws-sagemaker-estimators>`__
 7. `BYO Docker Containers with SageMaker Estimators <#byo-docker-containers-with-sagemaker-estimators>`__
-8. `BYO Model <#byo-model>`__
+8. `SageMaker Automatic Model Tuning <#sagmaker-automatic-model-tuning>`__
+9. `BYO Model <#byo-model>`__
 
 
 Getting SageMaker Python SDK
@@ -263,6 +264,56 @@ Please refer to the full example in the examples repo:
 The example notebook is is located here:
 ``advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb``
 
+
+SageMaker Automatic Model Tuning
+--------------------------------
+
+All of the estimators can be used with SageMaker Automatic Model Tuning, which performs hyperparameter tuning jobs. A hyperparameter tuning job runs multiple training jobs that differ by their hyperparameters to find the best one. The SageMaker Python SDK contains a ``HyperparameterTuner`` class for creating and interacting with hyperparameter training jobs.  You can read more about SageMaker Automatic Model Tuning in the `AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html>`__.
+
+Here is a basic example of how to use ``HyperparameterTuner`` to start tuning jobs instead of using an estimator to start training jobs:
+
+.. code:: python
+
+    from sagemaker.tuner import HyperparameterTuner, ContinuousParameter
+
+    # Configure HyperparameterTuner
+    my_tuner = HyperparameterTuner(estimator=my_estimator,  # previously-configured Estimator object
+                                   objective_metric_name='validation-accuracy',
+                                   hyperparameter_ranges={'learning-rate': ContinuousParameter(0.05, 0.06)},
+                                   metric_definitions=[{'Name': 'validation-accuracy', 'Regex': 'validation-accuracy=(\d\.\d+)'}],
+                                   max_jobs=100,
+                                   max_parallel_jobs=10)
+
+    # Start hyperparameter tuning job
+    my_tuner.fit({'train': 's3://my_bucket/my_training_data', 'test': 's3://my_bucket_my_testing_data'})
+
+    # Deploy best model
+    my_predictor = my_tuner.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')
+
+    # Make a prediction against the SageMaker endpoint
+    response = my_predictor.predict(my_prediction_data)
+
+    # Tear down the SageMaker endpoint
+    my_tuner.delete_endpoint()
+
+There is also an analytics object with each ``HyperparameterTuner`` instance, which presents useful information about the hyperparameter tuning job, like a pandas dataframe summarizing the associated training jobs:
+
+.. code:: python
+
+    # Retrieve analytics object
+    my_tuner_analytics = my_tuner.analytics()
+
+    # Look at summary of associated training jobs
+    my_dataframe = my_tuner_analytics.dataframe()
+
+For more detailed examples of running hyperparameter tuning jobs, see: https://github.com/awslabs/amazon-sagemaker-examples.
+
+For more detailed explanations of the classes mentioned, see:
+
+- `API docs for HyperparameterTuner and parameter range classes <https://sagemaker.readthedocs.io/en/latest/tuner.html>`__.
+- `API docs for analytics classes <https://sagemaker.readthedocs.io/en/latest/analytics.html>`__.
+
+
 FAQ
 ---