doc: remove some duplicated documentation from main README

README.rst

@@ -34,7 +34,7 @@ You can also train and deploy models with **Amazon algorithms**,
 which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training.
 If you have **your own algorithms** built into SageMaker compatible Docker containers, you can train and host models using these as well.
 
-For detailed API reference please go to: `Read the Docs <https://sagemaker.readthedocs.io>`_
+For detailed documentation, including the API reference, see `Read the Docs <https://sagemaker.readthedocs.io>`_.
 
 Table of Contents
 -----------------
@@ -45,11 +45,11 @@ Table of Contents
 #. `Using TensorFlow <https://sagemaker.readthedocs.io/en/stable/using_tf.html>`__
 #. `Using Chainer <https://sagemaker.readthedocs.io/en/stable/using_chainer.html>`__
 #. `Using PyTorch <https://sagemaker.readthedocs.io/en/stable/using_pytorch.html>`__
-#. `Scikit-learn SageMaker Estimators <#scikit-learn-sagemaker-estimators>`__
-#. `XGBoost SageMaker Estimators <#xgboost-sagemaker-estimators>`__
+#. `Using Scikit-learn <https://sagemaker.readthedocs.io/en/stable/using_sklearn.html>`__
+#. `Using XGBoost <https://sagemaker.readthedocs.io/en/stable/using_xgboost.html>`__
 #. `SageMaker Reinforcement Learning Estimators <https://sagemaker.readthedocs.io/en/stable/using_rl.html>`__
 #. `SageMaker SparkML Serving <#sagemaker-sparkml-serving>`__
-#. `AWS SageMaker Estimators <#aws-sagemaker-estimators>`__
+#. `Amazon SageMaker Built-in Algorithm Estimators <src/sagemaker/amazon/README.rst>`__
 #. `Using SageMaker AlgorithmEstimators <https://sagemaker.readthedocs.io/en/stable/overview.html#using-sagemaker-algorithmestimators>`__
 #. `Consuming SageMaker Model Packages <https://sagemaker.readthedocs.io/en/stable/overview.html#consuming-sagemaker-model-packages>`__
 #. `BYO Docker Containers with SageMaker Estimators <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-docker-containers-with-sagemaker-estimators>`__
@@ -58,9 +58,9 @@ Table of Contents
 #. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
 #. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
 #. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
-#. `Amazon SageMaker Operators for Kubernetes <#amazon-sagemaker-operators-for-kubernetes>`__
-#. `Amazon SageMaker Operators in Apache Airflow <#sagemaker-workflow>`__
-#. `SageMaker Autopilot <#sagemaker-autopilot>`__
+#. `Amazon SageMaker Operators for Kubernetes <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_operators_for_kubernetes.html>`__
+#. `Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>`__
+#. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
 #. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
 #. `SageMaker Debugger <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html>`__
 #. `SageMaker Processing <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html>`__
@@ -197,37 +197,6 @@ Preview the site with a Python web server:
 
 View the website by visiting http://localhost:8000
 
-Scikit-learn SageMaker Estimators
----------------------------------
-
-With Scikit-learn SageMaker Estimators, you can train and host Scikit-learn models on Amazon SageMaker.
-
-Supported versions of Scikit-learn: ``0.20.0``.
-
-We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
-
-For more information about Scikit-learn, see https://scikit-learn.org/stable/
-
-For more information about Scikit-learn SageMaker Estimators, see `Using Scikit-learn with the SageMaker Python SDK`_.
-
-.. _Using Scikit-learn with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_sklearn.html
-
-XGBoost SageMaker Estimators
-----------------------------
-
-With XGBoost SageMaker Estimators, you can train and host XGBoost models on Amazon SageMaker.
-
-Supported versions of XGBoost: ``0.90-1``.
-
-We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
-
-For more information about XGBoost, see https://xgboost.readthedocs.io/en/latest/
-
-For more information about XGBoost SageMaker Estimators, see `Using XGBoost with the SageMaker Python SDK`_.
-
-.. _Using XGBoost with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_xgboost.html
-
-
 SageMaker SparkML Serving
 -------------------------
 
@@ -260,45 +229,3 @@ For more information about the different ``content-type`` and ``Accept`` formats
 ``schema`` that SageMaker SparkML Serving recognizes, please see `SageMaker SparkML Serving Container`_.
 
 .. _SageMaker SparkML Serving Container: https://github.com/aws/sagemaker-sparkml-serving-container
-
-AWS SageMaker Estimators
-------------------------
-Amazon SageMaker provides several built-in machine learning algorithms that you can use to solve a variety of problems.
-
-The full list of algorithms is available at: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
-
-The SageMaker Python SDK includes estimator wrappers for the AWS K-means, Principal Components Analysis (PCA), Linear Learner, Factorization Machines,
-Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM), Random Cut Forest, k-nearest neighbors (k-NN), Object2Vec, and IP Insights algorithms.
-
-For more information, see `AWS SageMaker Estimators and Models`_.
-
-.. _AWS SageMaker Estimators and Models: src/sagemaker/amazon/README.rst
-
-Amazon SageMaker Operators for Kubernetes
------------------------------------------
-
-You can use Amazon SageMaker Operators for Kubernetes to optimize hyperparameters for a given model, run batch transform jobs over existing models, and set up inference endpoints.
-
-For more information, see `Amazon SageMaker Operators for Kubernetes`_.
-
-.. _Amazon SageMaker Operators for Kubernetes: https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_operators_for_kubernetes.html
-
-Amazon SageMaker Operators in Apache Airflow
---------------------------------------------
-
-You can use Apache Airflow to author, schedule and monitor SageMaker workflow.
-
-For more information, see `Amazon SageMaker Operators in Apache Airflow`_.
-
-.. _Amazon SageMaker Operators in Apache Airflow: https://sagemaker.readthedocs.io/en/stable/using_workflow.html
-
-SageMaker Autopilot
--------------------
-
-Amazon SageMaker Autopilot is an automated machine learning solution (commonly referred to as "AutoML") for tabular
-datasets. It automatically trains and tunes the best machine learning models for classification or regression based
-on your data, and hosts a series of models on an Inference Pipeline.
-
-For more information about SageMaker Autopilot, see `SageMaker Autopilot`_.
-
-.. _SageMaker Autopilot: src/sagemaker/automl/README.rst

doc/using_chainer.rst

@@ -6,7 +6,7 @@ With Chainer Estimators, you can train and host Chainer models on Amazon SageMak
 
 Supported versions of Chainer: ``4.0.0``, ``4.1.0``, ``5.0.0``.
 
-We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
+We recommend that you use the latest supported version because that's where we focus most of our development efforts.
 
 For more information about Chainer, see https://github.com/chainer/chainer.
 

doc/using_mxnet.rst

@@ -5,7 +5,6 @@ Use MXNet with the SageMaker Python SDK
 With the SageMaker Python SDK, you can train and host MXNet models on Amazon SageMaker.
 
 For information about supported versions of MXNet, see the `AWS documentation <https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html>`__.
-
 We recommend that you use the latest supported version because that's where we focus our development efforts.
 
 For general information about using the SageMaker Python SDK, see :ref:`overview:Using the SageMaker Python SDK`.

doc/using_sklearn.rst

@@ -4,7 +4,9 @@ Using Scikit-learn with the SageMaker Python SDK
 
 With Scikit-learn Estimators, you can train and host Scikit-learn models on Amazon SageMaker.
 
-For information about supported versions of Scikit-learn, see the `Chainer README <https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/sklearn/README.rst>`__.
+For information about supported versions of Scikit-learn, see the `Scikit-learn README <https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/sklearn/README.rst>`__.
+
+We recommend that you use the latest supported version because that's where we focus most of our development efforts.
 
 For general information about using the SageMaker Python SDK, see :ref:`overview:Using the SageMaker Python SDK`.
 

doc/using_tf.rst

@@ -2,9 +2,10 @@
 Using TensorFlow with the SageMaker Python SDK
 ##############################################
 
-TensorFlow SageMaker Estimators allow you to run your own TensorFlow
-training algorithms on SageMaker Learner, and to host your own TensorFlow
-models on SageMaker Hosting.
+With the SageMaker Python SDK, you can train and host TensorFlow models on Amazon SageMaker.
+
+For information about supported versions of TensorFlow, see the `AWS documentation <https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html>`__.
+We recommend that you use the latest supported version because that's where we focus our development efforts.
 
 For general information about using the SageMaker Python SDK, see :ref:`overview:Using the SageMaker Python SDK`.
 
@@ -20,9 +21,6 @@ For general information about using the SageMaker Python SDK, see :ref:`overview
 
 .. contents::
 
-Supported versions of TensorFlow for Elastic Inference: ``1.11``, ``1.12``, ``1.13``, ``1.14``, ``1.15``, ``2.0``.
-
-
 *****************************
 Train a Model with TensorFlow
 *****************************
@@ -40,7 +38,7 @@ To train a TensorFlow model by using the SageMaker Python SDK:
 3. |call fit|_
 
 Prepare a Script Mode Training Script
-======================================
+=====================================
 
 Your TensorFlow training script must be a Python 2.7-, 3.6- or 3.7-compatible source file.
 

doc/using_xgboost.rst

@@ -42,9 +42,13 @@ Use the Open Source XGBoost Algorithm
 
 If you want the flexibility and additional features that it provides, use the SageMaker open source XGBoost algorithm.
 
+For which XGBoost versions are supported, see `the AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html>`_.
+We recommend that you use the latest supported version because that's where we focus most of our development efforts.
+
 For a complete example of using the open source XGBoost algorithm, see the sample notebook at
 https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/xgboost_abalone/xgboost_abalone_dist_script_mode.ipynb.
 
+For more information about XGBoost, see `the XGBoost documentation <https://xgboost.readthedocs.io/en/latest>`_.
 
 Train a Model with Open Source XGBoost
 ======================================

src/sagemaker/amazon/README.rst

@@ -1,13 +1,13 @@
-
 ===================================
 AWS SageMaker Estimators and Models
 ===================================
 
 Amazon SageMaker provides several built-in machine learning algorithms that you can use for a variety of problem types.
+SageMaker Python SDK includes Estimators for many of these algorithms, including K-means, Principal Components Analysis (PCA),
+Linear Learner, Factorization Machines, Latent Dirichlet Allocation (LDA), Neural Topic Model (NTM), Random Cut Forest,
+k-nearest neighbors (k-NN), Object2Vec, and IP Insights.
 
-The full list of algorithms is available on the AWS website: https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html
-
-SageMaker Python SDK includes Estimator wrappers for the AWS K-means, Principal Components Analysis(PCA), Linear Learner, Factorization Machines, Latent Dirichlet Allocation(LDA), Neural Topic Model(NTM), Random Cut Forest algorithms, k-nearest neighbors (k-NN), Object2Vec, and IP Insights.
+For the full list of algorithms, visit `the AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html>`_.
 
 Definition and usage
 ~~~~~~~~~~~~~~~~~~~~

src/sagemaker/xgboost/README.rst

@@ -1,10 +1,11 @@
-============================================
+=======================================
 XGBoost SageMaker Estimators and Models
-============================================
+=======================================
 
 With XGBoost Estimators, you can train and host XGBoost models on Amazon SageMaker.
 
-Supported versions of SageMaker XGBoost: ``0.90-1``
+For which XGBoost versions are supported, see `the AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html>`_.
+We recommend that you use the latest supported version because that's where we focus most of our development efforts.
 
 Note that the first part of the version refers to the upstream module version (aka, 0.90), while the second
 part refers to the SageMaker version for the container.
@@ -14,7 +15,7 @@ You can visit the XGBoost repository at https://github.com/dmlc/xgboost
 For information about using XGBoost with the SageMaker Python SDK, see https://sagemaker.readthedocs.io/en/stable/using_xgboost.html.
 
 XGBoost Training Examples
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Amazon provides an example Jupyter notebook that demonstrate end-to-end training on Amazon SageMaker using XGBoost.
 Please refer to:
@@ -25,7 +26,7 @@ These are also available in SageMaker Notebook Instance hosted Jupyter notebooks
 
 
 SageMaker XGBoost Docker Containers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 When training and deploying training scripts, SageMaker runs your Python script in a Docker container with several
 libraries installed. When creating the Estimator and calling deploy to create the SageMaker Endpoint, you can control