Merge branch 'aws:master' into master

.github/ISSUE_TEMPLATE/example-request.md

@@ -18,3 +18,6 @@ A clear and concise description of the use case for this example.
 
 **Describe what other services (other than SageMaker) are involved***
 
+
+**Describe which dataset could be used. Provide its location in s3://sagemaker-sample-files or another source.**
+

README.md

@@ -60,8 +60,10 @@ These examples provide a gentle introduction to machine learning concepts as the
 These examples introduce SageMaker's hyperparameter tuning functionality which helps deliver the best possible predictions by running a large number of training jobs to determine which hyperparameter values are the most impactful.
 
 - [XGBoost Tuning](hyperparameter_tuning/xgboost_direct_marketing) shows how to use SageMaker hyperparameter tuning to improve your model fits for the [Targeted Direct Marketing](introduction_to_applying_machine_learning/xgboost_direct_marketing) task.
+- [BlazingText Tuning](hyperparameter_tuning/blazingtext_text_classification_20_newsgroups) shows how to use SageMaker hyperparameter tuning with the BlazingText built-in algorithm and 20_newsgroups dataset..
 - [TensorFlow Tuning](hyperparameter_tuning/tensorflow_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built TensorFlow container and MNIST dataset.
 - [MXNet Tuning](hyperparameter_tuning/mxnet_mnist) shows how to use SageMaker hyperparameter tuning with the pre-built MXNet container and MNIST dataset.
+- [HuggingFace Tuning](hyperparameter_tuning/huggingface_multiclass_text_classification_20_newsgroups) shows how to use SageMaker hyperparameter tuning with the pre-built HuggingFace container and 20_newsgroups dataset.
 - [Keras BYO Tuning](hyperparameter_tuning/keras_bring_your_own) shows how to use SageMaker hyperparameter tuning with a custom container running a Keras convolutional network on CIFAR-10 data.
 - [R BYO Tuning](hyperparameter_tuning/r_bring_your_own) shows how to use SageMaker hyperparameter tuning with the custom container from the [Bring Your Own R Algorithm](advanced_functionality/r_bring_your_own) example.
 - [Analyzing Results](hyperparameter_tuning/analyze_results) is a shared notebook that can be used after each of the above notebooks to provide analysis on how training jobs with different hyperparameters performed.
@@ -244,6 +246,7 @@ These examples show you how to use model-packages and algorithms from AWS Market
   - [Evaluating ML models from AWS Marketplace for person counting use case](aws_marketplace/using_model_packages/evaluating_aws_marketplace_models_for_person_counting_use_case) will show you how to use two AWS Marketplace GluonCV pre-trained ML models for person counting use case and evaluate each model for performance in different types of crowd images.
 - [Using Dataset Products](aws_marketplace/using_data)
   - [Using Dataset Product from AWS Data Exchange with ML model from AWS Marketplace](aws_marketplace/using_data/using_data_with_ml_model) is a sample notebook which shows how a dataset from AWS Data Exchange can be used with an ML Model Package from AWS Marketplace.
+  - [Using Shutterstock Image Datasets to train Image Classification Models](aws_marketplace/using_data/image_classification_with_shutterstock_image_datasets) provides a detailed walkthrough on how to use the [Free Sample: Images & Metadata of “Whole Foods” Shoppers](https://aws.amazon.com/marketplace/pp/prodview-y6xuddt42fmbu?qid=1623195111604&sr=0-1&ref_=srh_res_product_title#offers) from Shutterstock's Image Datasets to train a multi-label image classification model using Shutterstock's pre-labeled image assets. You can learn more about this implementation [from this blog post](https://aws.amazon.com/blogs/awsmarketplace/using-shutterstocks-image-datasets-to-train-your-computer-vision-models/).
 
 ## :balance_scale: License
 

advanced_functionality/README.md

@@ -8,6 +8,7 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [Encrypting Your Data](handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
 - [Using Parquet Data](parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume. 
 - [Connecting to Redshift](working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
+- [Bring Your Own scikit-learn Model](scikit_learn_bring_your_own_model) shows how to use Amazon SageMaker scikit-learn container to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
 - [Bring Your Own XGBoost Model](xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
 - [Bring Your Own k-means Model](kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it.
 - [Installing the R Kernel](install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.