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Project 4: Operationalizing a Machine Learning Microservice API

mAbdelFattah99

Project Overview

In this project, you will apply the skills learned in containers and Kubernetes orchestration to run the 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.

Required Files walkthrough

  • requirements.txt: dependencies to be installed.
  • app.py: The python API starter source code.
  • model_data/boston_housing_prediction.joblib: where the machine learning model file is stored.
  • Dockerfile: defination of the container content.
  • Makefile: the defination of the helper commands.
  • output_txt_files: required outputs are available in the this directory.

Running instructions

  • Create a virtualenv and activate it: python3 -m venv .devops-proj4 && source ~/.devops-proj4/bin/activate

  • Run make install to install the dependencies defined in requirements.txt file

  • optional test app.py: python app.py

  • run lint: make lint

  • build and upload docker by completing the two files ./run_docker.sh and ./upload_docker.sh, then

    1. Setup requirements for docker such as Login credentials.
    2. Run app.py in Docker: ./run_docker.sh
    3. Upload it: ./upload_docker.sh

  • Kubernetes instructions and steps as following:

    1. Setup requirements for kubernetes such as installing minikube and hypervisor.
    2. First start your minikube cluster: (minikube start)
    3. run script run_kubernetes.sh

  • Run sample query: execute the make_predictions.sh script (./make_predictions.sh).