https://github.com/parsh/operationalizemicroservice

ML MicroService Operationalization code for Udacity DevOps project
https://github.com/parsh/operationalizemicroservice

Last synced: 4 days ago
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ML MicroService Operationalization code for Udacity DevOps project

• Host: GitHub
• URL: https://github.com/parsh/operationalizemicroservice
• Owner: Parsh
• Created: 2020-04-06T03:01:38.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-06-22T01:36:50.000Z (over 2 years ago)
• Last Synced: 2024-10-12T20:38:21.773Z (about 1 month ago)
• Language: Python
• Size: 218 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
[![Parsh](https://circleci.com/gh/Parsh/OperationalizeMicroService.svg?style=svg)](https://circleci.com/gh/Parsh/OperationalizeMicroService)

## Project Overview

In this project, you will apply the skills you have acquired in this course to operationalize a 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](https://www.kaggle.com/c/boston-housing). 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](https://kubernetes.io/), 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](https://review.udacity.com/#!/rubrics/2576/view).

**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

- 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