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

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Setting up virtual environment

Installation, skip this step if you already have python installed

Download and install the python.

Setting up virtual environmentx

1. Create a virtual environment source ~/.devops/bin/activate
2. Activating virtural environment python3 -m venv ~/.devops

cd into project-ml-microservice-kubernetes directory and proceed with the following instructions

Installing dependencies

make install

Linting

make lint

Running Flask application

1. Standalone

python app.py

2. Run in docker

./run_docker.sh

3. Run in kubernetes

• ensure you have a virtual machine and minikube installedr
• start a local cluster by running the command minikube start
• finally, run ./run_kubernetes.sh to run app in kubernetes

Folder directory

All important files for the project are located in project-ml-microservice-kubernetes root directory

Output from running the app in various environments are found in output_txt_files

1. Output from running in docker is found in docker_out
2. Output from running in kubernetes is found in kubernetes_out

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