[](https://circleci.com/gh/udacity-class-question/DevOps-Microservice-Kubernetes)
## 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
# -DevOps-Microservice-Kubernetes