datamwin/operationalizing-machine-learning-api-as-a-microservice

Project Overview

This project seeks to operationalize a Machine Learning Microservice API. In this project a predictive model is rained with sklearn 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. The project operationalizes a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls.

Setup the Environment

• Create a virtualenv with Python 3.7 and activate it.

python3 -m pip install --user virtualenv

Use a command similar to this one:

python3 -m virtualenv --python=<path-to-Python3.7> .devops 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
  
## Files info
* run_docker.sh : This file builds a docker image and runs the app
* upload_docker.sh : this file deploys image to remote registry
* run_kubernetes.sh : This file runs the app with kubernetes
* make_prediction.sh : file used to test the app by making predictions
* Dockerfile : file for building images automatically by Docker
* Makefile : file for runing app related script like install and lint
* requirements.txt : file for installing dependencies for app