Operationalize-a-Machine-Learning-Microservice-API

Deploy a containerized Python flask application to serve out predictions (inference) about housing prices through API calls. It uses a a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features.

Install two important components

• Docker
• Virtualbox

I've done it on Mac with this command:

brew cask install virtualbox

• For Minikube you have to give this command (on Mac)

brew cask install minikube

How the project goes:

• First of all we have to test the code using lint
• Then we have to containerize our application, therefore we modify the Dockerfile
• After the containerization, we deploy the application and we make a prediction
• Configuring Kubernetes is our next step in order to create a Kubernetes cluster. To start a local cluster, type the terminal command:

minikube start

• We make a new prediction on a container using Kubernetes. To deploy this application in kubernetes run:

./run_kubernetes.sh

and in order to make a prediction:

./make_prediction.sh

• After the deployment is successful and we got the result, we can delete the cluster with:

minikube delete

and upload our GitHub repository

• We end the project successfuly after we have tested our code with CircleCI

A short explanation of our files in the repo

• config.yml: CircleCI special file to test the code and get a passed-badge
• app.py: main application, which calculates the predictions
• Dockerfile: Build Docker image
• make_prediction.sh: Send a request to Flask, in order to get the result of a prediction
• Makefile: commands which automatise the environment setup and tests
• run_docker.sh: run Docker
• run_kubernetes.sh: run app in Kubernetes
• upload_docker.sh: upload image to DockerHub