/Udacity_ML-API_Project

Final Project while taking ALX-Udacity Cloud devops nanodegree

Primary LanguagePython

Udacity_ML-API_Project

CircleCI

Project Overview

In this project, I applied the skills I have acquired in this course to operationalize a Machine Learning Microservice API.

I was 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 my 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

The 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:

  • Tested my project code using linting
  • Completed a Dockerfile to containerize this application
  • Deployed my containerized application using Docker and made a prediction
  • Improves the log statements in the source code for this application
  • Configured Kubernetes and created a Kubernetes cluster
  • Deployed a container using Kubernetes and made a prediction
  • Uploade a complete Github repo with CircleCI to indicate that my code has been tested

Environment setup, code test and lint

  • Run make all

Running app.py

  1. Standalone - python app.py
  2. On a docker container - ./run_docker.sh
  3. In a Kubernetes cluster - ./run_kubernetes.sh
    To run using option 3, a kubernetes cluster must be provided and setup

Brief description of files

  • model_data: Contains data used in training the sk_learn model
  • output_txt_files: contains logging output of ./run_docker.sh and ./run_kubernetes.sh commands
  • app.py: Machine learning flask app
  • Dockerfile: Contains instructions for building the docker container
  • make_predictions.sh: Bash script that send sample request to the running app
  • Makefile: includes instructions on environment setup and lint tests
  • requirements.txt: app environmental dependemcies
  • run_docker.sh: Bash script to run the application on a docker container
  • run_kubernetes.sh: Bash script to run the application in a kubernetes cluster
  • upload_docker.sh: Bash script to upload the built docker container to docker hub