Project Overview

In this project, I apply the skills I acquired to operationalize a Machine Learning Microservice API.

There is 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 the 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

My 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 I will:

• Test the project code using linting
• Complete a Dockerfile to containerize this application
• Deploy my 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 my code has been tested

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

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Files

• .circleci: For the CircleCI build server
• model_data : this folder contains the pretrained sklearn model and housing csv files
• output_txt_files: folder contains sample output logs from running ./run_docker.sh and ./run_kubernetes.sh
• app.py : contains the flask app
• Dockerfile: contains instructions to containerize the application
• Makefile : contains instructions for environment setup and lint tests
• requirements.txt: list of required dependencies
• run_docker.sh: bash script to build Docker image and run the application in a Docker container
• upload_docker.sh: bash script to upload the built Docker image to Dockerhub
• run_kubernetes.sh: bash script to run the application in a Kubernetes cluster
• make_prediction.sh: bash script to make predictions against the Docker container and k8s cluster
• README.md: this README file

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Setup the Environment

• Create a virtualenv with Python 3.7 and activate 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

• 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 Flask app in Container
• Run via kubectl