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Code for the DataCorps project with the Welcome Centre
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README.md

Welcome-Centre-DataCorps-Code

Code for the DataCorps project with the Welcome Centre

Code Structure


The TWC application contains the following core functionality:

  1. Transformers - Transform json formatted twc database records into a feature representation api.model.transformers
  2. Model Training - A script which trains an scikit-learn implemented random-forest-like model (ExtraTreesRegressor) to predict a score which indicates how likely a twc client is to exhibit dependent behaviour, i.e. frequent and/or long lasting usage of the service.
  3. API - A Flask-based REST API which provides endpoints to predict scores for new datasets, downloading models from Amazon S3, and controlling model state and retraining
  4. Serverless Deployment - Configuration for Zappa, which converts the API application into serverless AWS Lambda and sets the appropriate permissions for the IAM user in AWS
  5. Retraining Docker Application - A docker image specified in twc_api/Dockerfile, which builds an image which installs the required libraries for running the retraining script, and which is pushed to the twc ECS docker repository.

1. Transformers


api.model.transformers requires code to transform a twc database records into a standardised feature format that performs tasks such as the following:

  • Join data held in other tables to the Referral table
  • Create indicator variables for categorical values
  • Add referral context features (e.g. the number in burst)
  • Add time windowing functions
  • Calculating the target variable (dependency score)

This utilises a scikit-learn like transformer pattern, of stateful objects which implement both a fit_transform and transform method. Transformations which are stateful, e.g. creating dummy columns and maintaining the proper order of columns, store state as attributes in the transformer class itself. These transformer objects are serialised using the pickle library and used at test time to reliably transform unseen test datapoints to the exact format that the model was trained on.

2. Model Training


api.model.model defines the TWCModel object, which stores both the transformer and individual model object, and uses the standard scikit-learn interface, where fit(X, y) trains the model, and predict(X) returns predictions for new data.

3. API


We use the Flask framework and the flask-restplus library to define a RESTful API which allows applications to interact with the model. flask-restplus provides swagger documentation for ease of use.

Applications can access the following endpoints:

4. Serverless Deployment


To save on server costs, the API can be converted into AWS Lambda functions with the use of the Zappa library. This ensures that the machine provisioning is handled by Amazon and we do not require always on instances.

Running zappa update dev inside the twc_api directory will push any new code to the staging environment.

Additional permissions are granted to the zappa AWS IAM role to permit the API to launch new, larger, EC2 instances to retrain the model.

These are:

  • ec2.*
  • iam.PassRoles

The API once deployed, will be deployed in AWS' API Gateway, and the endpoint for access will be found there.

5. Retraining Docker Application


As inference requires fewer compute resources than training, and Lambda functions have a hard cap on permitted resources, we run the retraining process on a separate server instance which is ephemeral and deleted after training is completed.

In order to maintain a stable environment and versioning of the retrain logic, we use Docker to package up the dependencies of the retraining script and AWS Elastic Container Repository to store the image. The flow for retraining is as follows:

  1. Build and push the retraining docker image by following the instructions in the AWS ECR push commands (copied out below)
  2. Post a retraining request to the retrain endpoint
  3. API spins up a new EC2 instance which is large enough to complete the retraining and runs the following script which:
    1. Logs into ECR
    2. Pulls the latest Docker image
    3. Runs the retrain script and saves the results and any messages to S3
    4. Sets the current model to the new model

$(aws ecr get-login --no-include-email --region eu-west-1)

docker build -t twc .

docker tag twc:latest 213288821174.dkr.ecr.eu-west-1.amazonaws.com/twc:latest

docker push 213288821174.dkr.ecr.eu-west-1.amazonaws.com/twc:latest

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