diff --git a/doc/api/inference/model.rst b/doc/api/inference/model.rst
index 2846c58d0b..a975086162 100644
--- a/doc/api/inference/model.rst
+++ b/doc/api/inference/model.rst
@@ -15,3 +15,8 @@ Model
     :members:
     :undoc-members:
     :show-inheritance:
+
+.. autoclass:: sagemaker.serverless.model.LambdaModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/api/inference/predictors.rst b/doc/api/inference/predictors.rst
index 6a9243f329..66ffcf45be 100644
--- a/doc/api/inference/predictors.rst
+++ b/doc/api/inference/predictors.rst
@@ -7,3 +7,8 @@ Make real-time predictions against SageMaker endpoints with Python objects
     :members:
     :undoc-members:
     :show-inheritance:
+
+.. autoclass:: sagemaker.serverless.predictor.LambdaPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/overview.rst b/doc/overview.rst
index 02290ff94c..2085abd24d 100644
--- a/doc/overview.rst
+++ b/doc/overview.rst
@@ -1063,6 +1063,50 @@ You can also find these notebooks in the **Advanced Functionality** section of t
 For information about using sample notebooks in a SageMaker notebook instance, see `Use Example Notebooks <https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-nbexamples.html>`__
 in the AWS documentation.
 
+********************
+Serverless Inference
+********************
+
+You can use the SageMaker Python SDK to perform serverless inference on Lambda.
+
+To deploy models to Lambda, you must complete the following prerequisites:
+
+- `Package your model and inference code as a container image. <https://docs.aws.amazon.com/lambda/latest/dg/images-create.html>`_
+- `Create a role that lists Lambda as a trusted entity. <https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html#permissions-executionrole-console>`_
+
+After completing the prerequisites, you can deploy your model to Lambda using
+the `LambdaModel`_ class.
+
+.. code:: python
+
+   from sagemaker.serverless import LambdaModel
+
+   image_uri = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-lambda-repository:latest"
+   role = "arn:aws:iam::123456789012:role/MyLambdaExecutionRole"
+
+   model = LambdaModel(image_uri=image_uri, role=role)
+   predictor = model.deploy("my-lambda-function", timeout=20, memory_size=4092)
+
+The ``deploy`` method returns a `LambdaPredictor`_  instance. Use the
+`LambdaPredictor`_ ``predict`` method to perform inference on Lambda.
+
+.. code:: python
+
+   url = "https://example.com/cat.jpeg"
+   predictor.predict({"url": url})  # {'class': 'tabby'}
+
+Once you are done performing inference on Lambda, free the `LambdaModel`_ and
+`LambdaPredictor`_ resources using the ``delete_model`` and ``delete_predictor``
+methods.
+
+.. code:: python
+
+   model.delete_model()
+   predictor.delete_predictor()
+
+.. _LambdaModel : https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.serverless.model.LambdaModel
+.. _LambdaPredictor : https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.serverless.predictor.LambdaPredictor
+
 ******************
 SageMaker Workflow
 ******************