Machine Learning Microservice API

In this project, you will apply some skills to operationalize a Machine Learning Microservice API. We are given a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on.

Tasks

1. Test project code using linting
2. Complete a Dockerfile to containerize this application
3. Deploy containerized application using Docker and make a prediction
4. Improve the log statements in the source code for this application
5. Configure Kubernetes and create a Kubernetes cluster
6. Deploy a container using Kubernetes and make a prediction
7. Upload a complete Github repo with CircleCI to indicate that code has been tested successfully

How to run

• Run a Docker Container using the script ./run_docker.sh
• Upload the Docker Image using the script ./upload_docker.sh
• Deploy with Kubernetes using the script ./run_kubernetes.sh
• Make prediction either with Docker Container or Kubernetes Deployment using the script ./make_prediction.sh how to run the Python scripts and web app

List of Docker commands

https://docs.docker.com/engine/reference/commandline/docker/

Safely remove K8s Pods

https://www.bluematador.com/blog/safely-removing-pods-from-a-kubernetes-node

CricleCi blog

https://circleci.com/blog/triggering-trusted-ci-jobs-on-untrusted-forks/

https://circleci.com/docs/2.0/status-badges/