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‎.github/CODEOWNERS

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# Each line is a file pattern followed by one or more owners (@username or
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# email@address).
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#
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# Order is important. The last matching pattern has the most precedence.
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# So if a pull request only touches a specific folder, only the respective owners
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# will be requested to review.
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#
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# @See https://help.github.com/articles/about-codeowners/
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/sagemaker-experiments/* @aws/sagemakerexperimentsadmin
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/sagemaker-lineage/* @aws/sagemakerexperimentsadmin
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# Community contributed
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/contrib/ @aws/sagemaker-notebook-sas

‎.gitignore

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**/.ipynb_checkpoints
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**/.idea
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**/__pycache__
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**/.aws-sam
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.DS_Store
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**/_build

‎.readthedocs.yml

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# ReadTheDocs environment customization to allow us to use conda to install
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# libraries which have C dependencies for the doc build. See:
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# https://docs.readthedocs.io/en/latest/config-file/v2.html
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version: 2
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conda:
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environment: environment.yml
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python:
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version: 3.6
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sphinx:
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configuration: conf.py
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fail_on_warning: false

‎Makefile

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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line, and also
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# from the environment for the first two.
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SPHINXOPTS ?=
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SPHINXBUILD ?= sphinx-build
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SOURCEDIR = .
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BUILDDIR = _build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

‎README.md

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‎_static/js/analytics.js

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console.log("Starting analytics...");
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var s_code=s.t();if(s_code)document.write(s_code)

‎_static/product-icon_Amazon_SageMaker_lockup_centered_squid_ink.png

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‎_static/sagemaker_gears.jpg

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‎advanced_functionality/README.md

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- [Bring Your Own R Algorithm](r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.
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- [Bring Your Own scikit Algorithm](scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
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- [Bring Your Own MXNet Model](mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
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- [Bring Your Own TensorFlow Model](tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
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- [Bring Your Own TensorFlow Model](tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
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- [Inference Pipeline with SparkML and XGBoost](inference_pipeline_sparkml_xgboost_abalone) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and XGBoost for training on the Abalone dataset. The pre-processing code is written once and used between training and inference.
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- [Inference Pipeline with SparkML and BlazingText](inference_pipeline_sparkml_blazingtext_dbpedia) shows how to deploy an Inference Pipeline with SparkML for data pre-processing and BlazingText for training on the DBPedia dataset. The pre-processing code is written once and used between training and inference.

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