diff --git a/README.rst b/README.rst
index acbc2458dc..57ead60fea 100644
--- a/README.rst
+++ b/README.rst
@@ -45,7 +45,7 @@ With `virtualenv`_, it's possible to install this library without needing system
 install permissions, and without clashing with the installed system
 dependencies.
 
-.. _`virtualenv`: https://virtualenv.pypa.io/en/latest/
+.. _virtualenv: https://virtualenv.pypa.io/en/latest/
 
 
 Mac/Linux
@@ -69,6 +69,253 @@ Windows
     <your-env>\Scripts\activate
     <your-env>\Scripts\pip.exe install google-cloud-aiplatform
 
+
+Overview
+~~~~~~~~
+This section provides a brief overview of the Vertex SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.
+
+.. _vertex-ai-samples: https://github.com/GoogleCloudPlatform/ai-platform-samples/tree/master/ai-platform-unified/notebooks/unofficial/sdk
+
+Importing
+^^^^^^^^^
+SDK functionality can be used from the root of the package:
+
+.. code-block:: Python
+
+    from google.cloud import aiplatform
+
+
+Initialization
+^^^^^^^^^^^^^^
+Initialize the SDK to store common configurations that you use with the SDK.
+
+.. code-block:: Python
+
+    aiplatform.init(
+        # your Google Cloud Project ID or number
+        # environment default used is not set
+        project='my-project',
+
+        # the Vertex AI region you will use
+        # defaults to us-central1
+        location='us-central1',
+
+        # Googlge Cloud Stoage bucket in same region as location
+        # used to stage artifacts
+        staging_bucket='gs://my_staging_bucket',
+
+        # custom google.auth.credentials.Credentials
+        # environment default creds used if not set
+        credentials=my_credentials,
+
+        # customer managed encryption key resource name
+        # will be applied to all Vertex AI resources if set
+        encryption_spec_key_name=my_encryption_key_name,
+
+        # the name of the experiment to use to track
+        # logged metrics and parameters
+        experiment='my-experiment',
+
+        # description of the experiment above
+        experiment_description='my experiment decsription' 
+    )
+
+Datasets
+^^^^^^^^
+Vertex AI provides managed tabular, text, image, and video datasets. In the SDK, datasets can be used downstream to
+train models.
+
+To create a tabular dataset:
+
+.. code-block:: Python
+
+    my_dataset = aiplatform.TabularDataset.create(
+        display_name="my-dataset", gcs_source=['gs://path/to/my/dataset.csv'])
+
+You can also create and import a dataset in separate steps:
+
+.. code-block:: Python
+
+    from google.cloud import aiplatform
+
+    my_dataset = aiplatform.TextDataset.create(
+        display_name="my-dataset")
+
+    my_dataset.import(
+        gcs_source=['gs://path/to/my/dataset.csv']
+        import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
+    )
+
+To get a previously created Dataset:
+
+.. code-block:: Python
+  
+  dataset = aiplatform.ImageDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
+
+Vertex AI supports a variety of dataset schemas. References to these schemas are available under the
+:code:`aiplatform.schema.dataset` namespace. For more information on the supported dataset schemas please refer to the
+`Preparing data docs`_.
+
+.. _Preparing data docs: https://cloud.google.com/ai-platform-unified/docs/datasets/prepare
+
+Training
+^^^^^^^^
+The Vertex SDK for Python allows you train Custom and AutoML Models.
+
+You can train custom models using a custom Python script, custom Python package, or container.
+
+**Preparing Your Custom Code**
+
+Vertex AI custom training enables you to train on Vertex AI datasets and produce Vertex AI models. To do so your
+script must adhere to the following contract:
+
+It must read datasets from the environment variables populated by the training service:
+
+.. code-block:: Python
+
+  os.environ['AIP_DATA_FORMAT']  # provides format of data  
+  os.environ['AIP_TRAINING_DATA_URI']  # uri to training split
+  os.environ['AIP_VALIDATION_DATA_URI']  # uri to validation split
+  os.environ['AIP_TEST_DATA_URI']  # uri to test split
+
+Please visit `Using a managed dataset in a custom training application`_ for a detailed overview.
+
+.. _Using a managed dataset in a custom training application: https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets
+
+It must write the model artifact to the environment variable populated by the traing service:
+
+.. code-block:: Python 
+
+  os.environ['AIP_MODEL_DIR']
+
+**Running Training**
+
+.. code-block:: Python
+
+  job = aiplatform.CustomTrainingJob(
+      display_name="my-training-job",
+      script_path="training_script.py",
+      container_uri="gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest",
+      requirements=["gcsfs==0.7.1"],
+      model_serving_container_image_uri="gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest",
+  )
+
+  model = job.run(my_dataset,
+                  replica_count=1,
+                  machine_type="n1-standard-4",
+                  accelerator_type='NVIDIA_TESLA_K80',
+                  accelerator_count=1)
+
+In the code block above `my_dataset` is managed dataset created in the `Dataset` section above. The `model` variable is a managed Vertex AI model that can be deployed or exported.
+
+
+AutoMLs
+-------
+The Vertex SDK for Python supports AutoML tabular, image, text, video, and forecasting.
+
+To train an AutoML tabular model:
+
+.. code-block:: Python
+
+  dataset = aiplatform.TabularDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
+
+  job = aiplatform.AutoMLTabularTrainingJob(
+    display_name="train-automl",
+    optimization_prediction_type="regression",
+    optimization_objective="minimize-rmse",
+  )
+
+  model = job.run(
+      dataset=dataset,
+      target_column="target_column_name",
+      training_fraction_split=0.6,
+      validation_fraction_split=0.2,
+      test_fraction_split=0.2,
+      budget_milli_node_hours=1000,
+      model_display_name="my-automl-model",
+      disable_early_stopping=False,
+  )
+
+
+Models
+------
+
+To deploy a model:
+
+
+.. code-block:: Python
+
+  endpoint = model.deploy(machine_type="n1-standard-4",
+                          min_replica_count=1,
+                          max_replica_count=5
+                          machine_type='n1-standard-4',
+                          accelerator_type='NVIDIA_TESLA_K80',
+                          accelerator_count=1)
+
+
+To upload a model:
+
+.. code-block:: Python
+
+  model = aiplatform.Model.upload(
+      display_name='my-model',
+      artifact_uri="gs://python/to/my/model/dir",
+      serving_container_image_uri="gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest",
+  )
+
+To get a model:
+
+.. code-block:: Python
+
+  model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
+
+Please visit `Importing models to Vertex AI`_ for a detailed overview:
+
+.. _Importing models to Vertex AI: https://cloud.google.com/vertex-ai/docs/general/import-model
+
+
+Endpoints
+---------
+
+To get predictions from endpoints:
+
+.. code-block:: Python
+
+  endpoint.predict(instances=[[6.7, 3.1, 4.7, 1.5], [4.6, 3.1, 1.5, 0.2]])
+
+
+To create an endpoint
+
+.. code-block:: Python
+
+  endpoint = endpoint.create(display_name='my-endpoint')
+
+To deploy a model to a created endpoint:
+
+.. code-block:: Python
+
+  model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
+  
+  endpoint.deploy(model,
+                  min_replica_count=1,
+                  max_replica_count=5
+                  machine_type='n1-standard-4',
+                  accelerator_type='NVIDIA_TESLA_K80',
+                  accelerator_count=1)
+
+To undeploy models from an endpoint:
+
+.. code-block:: Python
+
+  endpoint.undeploy_all()
+
+To delete an endpoint:
+
+.. code-block:: Python
+  
+  endpoint.delete()
+
+
 Next Steps
 ~~~~~~~~~~