Adding workshop material for v1 release

Author: bennydee-aws

.gitignore

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+.DS_Store
+.vscode
+.ipynb_checkpoints/
+.aws-sam/

README.md

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-## My Project
+# Biological Cell Segmentation using Amazon SageMaker
 
-TODO: Fill this README out!
+> Please see the AWS Workshop for a complete end-to-end tutorial on using this repository. https://catalog.us-east-1.prod.workshops.aws/workshops/7fb985db-2c2c-4f72-8aa6-7a1c8202b61a
 
-Be sure to:
+This workshop outlines a machine-learning cell segmentation architecture from scratch for the life-sciences vertical. The use-case is tailored towards having a particular cell of interest from the lab (e.g human embryos, hepatocytes cells, etc) and wish to determine the number of cells, density and basic characteristics of the sample from a microscopy image.
 
-* Change the title in this README
-* Edit your repository description on GitHub
+The workshop is expected to take *3 hours*, aimed at individuals who want to learn how Machine Learning can help make predications based on open data. No specific background knowledge is required. The workshop provides step-by-step instructions along with the code required to run each step to cover the following:
+
+* Downloading the dataset from the Broad Bioimage Benchmark Collection (BBBC005) which will be used for training purposes to build our model.
+* Demonstrate the process to use a SageMaker Notebook to train a model form scratch, specifically for cell-segmentation.
+* Setting up an SageMaker Inference endpoint to host our model.
+* Configuring S3 with event notifications, which will trigger a Lambda function which will invoke our SageMaker inference endpoint for processing an image to determine the cell segmentation.
+
+The aim of the workshop is to introduce building a model from scratch and how to use Amazon SageMaker to train and host a model, using a serverless architecture for processing images uploaded to an S3 bucket which interface with a SageMaker inference endpoint.
+
+## Training architecture
+
+The architecture for training the model consists of the following:
+
+![Training](/static/training.png)
+
+## Inference architecture
+
+The architecture for running inference consists of the following:
+
+![Inference](/static/inference-v1.png)
+
+## Next steps
+
+Please see the AWS Workshop for a complete tutorial on using this repository. 
+
+https://catalog.us-east-1.prod.workshops.aws/workshops/7fb985db-2c2c-4f72-8aa6-7a1c8202b61a
 
 ## Security
 
 See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
 
 ## License
 
-This library is licensed under the MIT-0 License. See the LICENSE file.
-
+This library is licensed under the MIT-0 License. See the LICENSE file.

THIRD-PARTY-NOTICES

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+The Python U-Net Tensorflow implementation is adapted by [Kjetil Åmdal-Sævik](https://www.kaggle.com/keegil/keras-u-net-starter-lb-0-277) under an Apache 2.0 open-source license.