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:

$ make setup

• Install the necessary dependencies:

$ make install

• Fill and set env variables up:

$ sh setup_env.sh

Running app.py

There are two ways for running app.py: locally, docker and kubernetes.

Locally

• Run app.py

$ python app.py

Running under docker

• The first way to run prediction under docker is running run_docker.sh:

$ sh run_docker.sh

• The easiest way is running by makefile:

$ make docker

Running under kubernetes

There are two ways to deploy prediction to kubernetes: running run_kubernetes.sh script or applying by yaml file using makefile.

• To run prediction app under kubernetes, build a docker image and/or push this image to your dockerhub using run_kubernetes.sh script:

$ make docker
$ sh run_kubernetes.sh

• To run prediction app under kubernetes, build a docker image and/or push this image to your dockerhub using yaml/makefile:

$ make docker
$ make kubernetes