major update of the readme

Author: MalteKurz

README.md

@@ -1,3 +1,65 @@
 # DoubleML-Serverless - Distributed Double Machine Learning with a Serverless Architecture
 
+This repo contains a prototype implementation **DoubleML-Serverless** of distributed double machine learning with a serverless infrastructure
+using [AWS Lambda](https://aws.amazon.com/lambda).
+A detailed discussion of this prototype can be found in the paper "Distributed Double Machine Learning with a  Serverless Architecture" (Kurz, 2021).
+**DoubleML-Serverless** is an extension for serverless cloud computing of the Python package **DoubleML**.
+**DoubleML** is available via PyPI [https://pypi.org/project/DoubleML](https://pypi.org/project/DoubleML) and on GitHub [https://github.com/DoubleML/doubleml-for-py](https://github.com/DoubleML/doubleml-for-py).
+Also see [https://docs.doubleml.org](https://docs.doubleml.org) for a detailed documentation and user guide for the **DoubleML** package.
 
+## Getting started
+
+### Installation of DoubleML-Serverless
+
+To install download the latest source code from GitHub via
+```
+git clone git@github.com:DoubleML/doubleml-serverless.git
+cd doubleml-serverless
+```
+
+Then build the package from source using pip in the editable mode.
+
+```
+pip install --editable .
+```
+
+Alternatively to the installation from source, released versions of the DoubleML-Serverless package in form of
+.whl files can be obtained from [GitHub Releases](https://github.com/DoubleML/doubleml-serverless/releases).
+After downloading the wheel, the package can be installed with pip (replace `XXX` with the downloaded package version).
+```
+pip install -U DoubleML-Serverless-XXX-py3-none-any.whl
+```
+
+### Deploy the corresponding serverless app to AWS Lambda using AWS SAM
+
+To use AWS Lambda for estimating double machine learning models, a deployment in your AWS account is necessary.
+The corresponding serverless application consists of the following components:
+
+* A AWS Lambda function called `LambdaCVPredict` (the source code is taken from this repository [https://github.com/DoubleML/doubleml-serverless/blob/master/aws_lambda_app/lambda_functions/cv_predict.py](https://github.com/DoubleML/doubleml-serverless/blob/master/aws_lambda_app/lambda_functions/cv_predict.py)).
+* A layer providing the Python libraries `scikit-learn`, `pandas` and `numpy` together with their dependencies.
+* An S3 bucket for the data transfer (can be optionally generated, or an existing bucket is used).
+* A role for the execution of the lambda function `LambdaCVPredict` which consists of the AWS-managed `AWSLambdaBasicExecutionRole` policy plus read access to the S3 bucket for data transfer.
+
+
+There are two options for deployment:
+
+1. A version of DoubleML-Serverless is available in the AWS Serverless Application Repository. It can be deployed by clicking on the `Deploy` button.
+
+2. The second option for deployment is based on AWS Serverless Application Model (AWS SAM).
+
+2.1 Setup the AWS SAM CLI as described here: [https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-getting-started.html](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-getting-started.html)
+
+2.2 To deploy the application use the following commands (for more information see [https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/what-is-sam.html](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/what-is-sam.html))
+
+```
+cd aws_lambda_app
+sam build
+sam deploy --guided
+```
+
+### Estimating a partially linear regression model with double machine learning and serverless scaling using AWS Lambda
+
+
+
+## References
+Kurz, M.S. 2020. "Distributed Double Machine Learning with a  Serverless Architecture". Unpublished Working Paper.