guillermo-ampie / ml-microservice-kubernetes

Operationalize a Machine Learning Microservice API

Operationalize a Machine Learning Microservice API

Project Overview

This project uses 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.

Introduction

This project is part of Udacity - AWS Cloud DevOps Engineer

Code forked from: https://github.com/udacity/DevOps_Microservices.git

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: make lint
• Complete a Dockerfile to containerize this application: Dockerfile
• Deploy your containerized application using Docker and make a prediction
  • run_docker.sh
    • Sample output: docker_out.txt
  • make_prediction.sh
    • Sample output prediction_out.txt
• Improve the log statements in the source code for this application: app.py
• Configure Kubernetes and create a Kubernetes cluster
  • Upload your container to DockerHub: upload_docker.sh
  • Install Docker
  • Install kubectl(for macOS): brew install kubectl
  • Install Minikube (for macOS): brew cask install minikube
  • Start minikube: minikube start
  • Check your cluster: kubectl config view
• Deploy a container using Kubernetes and make a prediction
  • run_kubernetes.sh
    • Sample output: kubernetes_out.txt
  • make_prediction.sh
    • Sample output: prediction_out.txt
• Upload a complete Github repo with CircleCI to indicate that your code has been tested
• CircleCI config file: .circleci/config.yml

You can find a detailed project rubric, here.

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

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Setup the Environment

• Create a virtualenv and activate it
  • make setup
  • 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

Main files

• The python Flask app: app.py
• The sklearn pre-trained model: boston_housing_prediction.joblib
• The Dockerfile to containerize the app: Dockerfile
• To make and run your container: run_docker.sh
• To make a prediction: make_prediction.sh
• To upload your container to DockerHub: upload_docker.sh
• To deploy a container using Kubernetes and make a prediction
  • run_kubernetes.sh
  • make_prediction.sh
• To integrate and test your app under CircleCI: config.yml