Description

The goal of the project is to operationalize a Machine Learning application as a microservice using Docker and Kubernetes. A pre-trained model for predicting housing prices in Boston is used (more about the data initially taken from Kaggle, on the data source site.

A Python flask application serves out predictions (inference) about housing prices through API calls. The code is packaged into a docker container and deployed to AWS. Next to docker, a kubernetes cluster is also defined and deployed. Additionally, CI/CD principles are utilizes, such as linting the python code, as well as the docker container and deployment testing via CircleCI SaaS solution.

Steps:

• Create a virtualenv and activate it

• Run make install to install the necessary dependencies

• Test your project code using linting

  • in the Makefile the linting executed via pylint (for the Flask app) and via hadolint for the Dockerfile

• Complete a Dockerfile to containerize this application

  • in the Dokcerfile the initail container is defined and the files of the applications are copied
  • port 80 is expoded
  • CMD executes the program upon creation of the container

• Deploy your containerized application using Docker and make a prediction

  • the docker image is build `docker build --tag=mlapp .``
  • for a local prediction the app is run from inside the docker docker run -p 8000:5001 mlapp
  • The container is uploaded to AWS ECR and the application (mlapp) is reachable via the EC2 public address
  • The container is also uploaded to Docker Hub, for the next step of running the Docker with Kubernetes
  • steps are in run_docker.sh, output is captured in the docker_out.txt

• Configure Kubernetes and create a Kubernetes cluster

  • the kubectl and minicube have been installed in the cloud9 environment

• Deploy a container using Kubernetes and make a prediction

  • start the kubernetes cluster locally using minicube
  • define the path to the docker image on the Docker Hub
  • run kubectl for from the image from the Docker Hub
  • forward the port to the container and execute the make_prediction.sh
  • steps are in run_kubernetes.sh, output is captured in kubernetes_out.txt

• Upload a complete Github repo with CircleCI to indicate that your code has been tested

  • the project is uploaded to github and connected to circeCI

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Running the Flask application mlapp.py

1. Standalone: python mlapp.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh