Operationalizing a Machine Learning Microservice API

Project Overview

This project demonstrates the operationalization of a Machine Learning Microservice API using Kubernetes. The core application is a Python Flask web application (app.py) that serves inference predictions about housing prices through API calls. It utilizes a pre-trained sklearn machine learning model trained on Kaggle data to predict Boston housing prices based on features like average rooms, teacher-to-pupil ratios, and highway access.

Project Tasks

Your project goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. In this project you will:

• Test your project code using linting
• Complete a Dockerfile to containerize this application
• Deploy your containerized application using Docker and make a prediction
• Improve the log statements in the source code for this application
• Configure Kubernetes and create a Kubernetes cluster
• Deploy a container using Kubernetes and make a prediction
• Upload a complete Github repo with CircleCI to indicate that your code has been tested

You can find a detailed project rubric, here.

Required Files walkthrough

• requirements.txt: dependencies to be installed.
• app.py: The python API starter source code.
• model_data/boston_housing_prediction.joblib: where the machine learning model file is stored.
• Dockerfile: defination of the container content.
• Makefile: the defination of the helper commands.
• output_txt_files: required outputs are available in the this directory.

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Running instructions

• Create a virtualenv and activate it: python3 -m venv .devops-proj4 && source ~/.devops-proj4/bin/activate

• Run make install to install the dependencies defined in requirements.txt file

• optional test app.py: python app.py

• run lint: make lint

• build and upload docker by completing the two files ./run_docker.sh and ./upload_docker.sh, then

  1. Setup requirements for docker such as Login credentials.
  2. Run app.py in Docker: ./run_docker.sh
  3. Upload it: ./upload_docker.sh

• Kubernetes instructions and steps as following:

  1. Setup requirements for kubernetes such as installing minikube and hypervisor.
  2. First start your minikube cluster: (minikube start)
  3. run script run_kubernetes.sh

• Run sample query: execute the make_predictions.sh script (./make_predictions.sh).