Get started updates (aws#3335)

* moved and updated getting started content

* remove caption

Co-authored-by: Julia Kroll <[email protected]>

Author: aaronmarkham

index.rst

@@ -11,7 +11,6 @@ Welcome to Amazon SageMaker.
 This site highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker.
 
 This site is based on the `SageMaker Examples repository <https://github.com/aws/amazon-sagemaker-examples>`_ on GitHub.
-Browse around to see what piques your interest.
 To run these notebooks, you will need a SageMaker Notebook Instance or SageMaker Studio.
 Refer to the SageMaker developer guide's `Get Started <https://docs.aws.amazon.com/sagemaker/latest/dg/gs.html>`_ page to get one of these set up.
 
@@ -22,10 +21,9 @@ On SageMaker Studio, you will need to open a terminal, go to your home folder, t
 
 
 .. toctree::
-   :maxdepth: 1
-   :caption: Get started
+  :maxdepth: 1
 
-   get_started/index
+  intro.rst
 
 
 .. toctree::

get_started/index.rst renamed to intro.rst

@@ -1,12 +1,12 @@
-Get Started with Amazon SageMaker
+Introduction to Amazon SageMaker
 =================================
 
-You have several options for how you can use SageMaker.
+You have several options for how you can use Amazon SageMaker.
 
-1. IDE: SageMaker Studio
-2. Console: SageMaker Notebook Instances
-3. Command line & SDK: AWS CLI, boto3, & SageMaker Python SDK
-4. 3rd party integrations: Kubeflow & Kubernetes operators
+1. IDE: `SageMaker Studio <https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html>`_
+2. Console: `SageMaker Notebook Instances <https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks.html>`_
+3. Command line & SDK: `AWS CLI <https://docs.aws.amazon.com/cli/latest/reference/sagemaker/index.html#cli-aws-sagemaker>`_, `boto3 <https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html>`_, & `SageMaker Python SDK <https://sagemaker.readthedocs.io/>`_
+4. 3rd party integrations: `Kubeflow <https://docs.aws.amazon.com/sagemaker/latest/dg/kubernetes-sagemaker-components-for-kubeflow-pipelines.html>`_ & `Kubernetes operators <https://docs.aws.amazon.com/sagemaker/latest/dg/kubernetes-sagemaker-operators.html>`_
 
 If you're new to SageMaker we recommend starting with more feature-rich SageMaker Studio.
 It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations.
@@ -42,33 +42,3 @@ Get started with SageMaker Notebook Instances
     <div style="position: relative; padding-bottom: 5%; height: 0; overflow: hidden; max-width: 100%; height: auto;">
         <iframe width="560" height="315" src="https://www.youtube.com/embed/X5CLunIzj3U" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
     </div>
-
-
-Introduction to applying machine learning
-=========================================
-
-.. toctree::
-   :maxdepth: 1
-
-   ../introduction_to_applying_machine_learning/video_game_sales/video-game-sales-xgboost
-   ../introduction_to_applying_machine_learning/breast_cancer_prediction/Breast Cancer Prediction
-   ../introduction_to_applying_machine_learning/xgboost_customer_churn/xgboost_customer_churn
-
-
-Training
-=================================
-
-.. toctree::
-   :maxdepth: 1
-
-   ../introduction_to_amazon_algorithms/xgboost_mnist/xgboost_mnist
-   ../introduction_to_applying_machine_learning/ensemble_modeling/EnsembleLearnerCensusIncome
-
-
-Inference
-=========
-
-.. toctree::
-   :maxdepth: 1
-
-   ../introduction_to_amazon_algorithms/blazingtext_hosting_pretrained_fasttext/blazingtext_hosting_pretrained_fasttext