Update README.md

Author: eitansela

README.md

@@ -62,7 +62,7 @@ The repository contains the following resources:
  - [**PyTorch Script Mode Deploy a Trained Model**](pytorch_script_mode_local_model_inference): This example shows how to deploy a trained model to a SageMaker endpoint, on your local machine using SageMaker local mode, and serve your model with the SageMaker Endpoint.
  - [**Deploy a pre-trained PyTorch HeBERT model from Hugging Face on Amazon SageMaker Endpoint**](huggingface_hebert_sentiment_analysis_local_serving): This example shows how to deploy a pre-trained PyTorch HeBERT model from Hugging Face, on Amazon SageMaker Endpoint, on your local machine using SageMaker local mode.
 
-- **Bring Your Own Container resources:** 
+- **Bring Your Own Container Training resources:** 
 
  - [**Bring Your Own Container TensorFlow Algorithm - Train/Serve**](tensorflow_bring_your_own_california_housing_local_training_and_serving): This example provides a detailed walkthrough on how to package a Tensorflow 2.5.0 algorithm for training and production-ready hosting. We have included also a Python file for local training and serving that can run on your local computer, for faster development.
  - [**Bring Your Own Container TensorFlow Algorithm - Train/Batch Transform**](tensorflow_bring_your_own_california_housing_local_training_and_batch_transform): This example provides a detailed walkthrough on how to package a Tensorflow 2.5.0 algorithm for training, and then run a Batch Transform job on a CSV file. We have included also a Python file for local training and serving that can run on your local computer, for faster development.
@@ -74,14 +74,15 @@ The repository contains the following resources:
  - [**Bring Your Own Container scikit-learn Algorithm and deploy a pre-trained Model**](scikit_learn_bring_your_own_container_and_own_model_local_serving): This example provides a detailed walkthrough on how to package a scikit-learn algorithm for serving with a pre-trained model. We have included also a Python file for local serving that can run on your local computer, for faster development.
  - [**Delta Lake Bring Your Own Container CatBoost Algorithm**](delta_lake_bring_your_own_container_local_training_and_serving): This example provides a detailed walkthrough on how to package a CatBoost algorithm for training on data fetched from Delta Lake, directly from S3, and then serve your model with the Docker image you have built. We have included also a Python file for local training and serving that can run on your local computer, for faster development. 
 
-- **Built-in scikit-learn Processing Job :** 
+- **Built-in scikit-learn Processing Job resources:** 
 
  - [**Built-in scikit-learn Processing Job**](scikit_learn_local_processing): This example provides a detailed walkthrough on how to use the built-in scikit-learn Docker image for processing jobs. We have included also a Python file for processing jobs that can run on your local computer, for faster development.
 
-- **Bring Your Own Container scikit-learn Processing Job :** 
+- **Bring Your Own Container Processing Job resources:** 
 
  - [**Bring Your Own Container scikit-learn Processing Job**](scikit_learn_bring_your_own_container_local_processing): This example provides a detailed walk-through on how to package a scikit-learn Docker image for processing jobs. We have included also a Python file for processing jobs that can run on your local computer, for faster development.
  - [**Delta Sharing Bring Your Own Container Processing Job**](delta_sharing_bring_your_own_container_local_processing): This example provides a detailed walk-through on how to package a scikit-learn Docker image for processing job that fetch data from a table on Delta Lake using Delta Sharing, and aggregate total COVID-19 cases per country. We have included also a Python file for processing jobs that can run on your local computer, for faster development.
+ - [**Dask Bring Your Own Container Processing Job**](dask_bring_your_own_container_local_processing): This example provides a detailed walk-through on how to package a Dask Docker image for processing job that fetch JSON file from a website, and outputs the filenames found. We have included also a Python file for processing jobs that can run on your local computer, for faster development.
 
 **Note**: Those examples were tested on macOS and Linux.