Project Overview

In this project, I used the skill I acquired in this course to operationalize a Machine Learning Microservice API.

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

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

Setup the Environment

• I created a virtualenv with Python 3.7 and activated it. Refer to this link for help on specifying the Python version in the virtualenv.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# Use a command similar to this one:
python3 -m virtualenv --python=<path-to-Python3.7> .devops
source .devops/bin/activate

• I Ran 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 Flask app in Container
• Run via kubectl

Explanation of files

1. circleci/config.yml ---- circleci configuration

2. Dockerfile ---- Docker configuration file

3. make_prediction.sh ---- A script for logging predictions endpoint output

4. Makefile ---- This contains instructions on environment setup,dependencies installation instruction, and lint tests

5. requirements.txt ---- This contains python dependencies for the project

6. run_docker.sh ---- A shell script to build docker image and run it

7. model_data ---- contains housing prices in Boston area

8. output_txt_files/docker_out.txt ---- contains docker log outputs

9. output_txt_files/kubernetes_out.txt ---- contains kubernetes log outputs

10. app.py - flask app API endpoint with routes to get house prices in Boston

11. run_kubernetes.sh ----- A shell script for running Docker Hub container with kubernetes

12. upload_docker.sh ---- A shell script for uploading local docker build image to docker hub that is online.