Project Overview

In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.

You 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. You can read more about the data, which was initially taken from Kaggle, on the data source site. This project tests your ability to operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

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.

The final implementation of the project will showcase your abilities to operationalize production microservices.

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Setup the Environment

• Create a virtualenv and activate it by executing python3 -m venv venv
• Source the virtual environment: source venv/bin/activate
• Run make install to install the necessary dependencies

Running app.py

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

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create a kubernetes secret to store the credentials of the private Docker registry

    kubectl create secret docker-registry regcred \
        --docker-server=https://index.docker.io/v1/ \
        --docker-username=<DOCKERHUB_USERNAME> \
        --docker-password=<DOCKERHUB_PASSWORD> \
        --docker-email=<DOCKER_EMAIL>

• Create Flask app in Container
• Run via kubectl

Directory Structure

Directory/File	Description
.circleci/config.yml	CircleCI configuration
model_data	Trained model data for housing prices in Boston
output_txt_files	Docker and Kubernetes log output
app.py	REST Endpoint for predicting housing prices in Boston
Dockerfile	Dockerfile containing the application and its dependencies
make_prediction.sh	Calls prediction REST endpoint and simulates sample prediction
Makefile	Build file of the project
requirements.txt	Python requirements
run_docker.sh	Shell script for creating and running docker container
run_kubernetes.sh	Shell script to deploy docker container on Kubernetes cluster
upload_docker.sh	Shell script for uploading locally built docker image to dockerhub repository