Machine Learning Microservice

Project Overview

In this project we operationalize a Machine Learning Microservice API.

We 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 our 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

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

Setup the Environment

Create a virtual environment and activate it

python3 -m venv ~/.devops
source ~/.devops/bin/activate

Install the necessary dependencies from Makefile
```
make install
```

Running `app.py`

Standalone: python app.py
Run in Docker: ./run_docker.sh
Run in Kubernetes: ./run_kubernetes.sh

Make a Prediction

Open a new terminal and run:
```
./make_prediction.sh
```

Kubernetes Steps

Setup and Configure Docker locally
Setup and Configure Kubernetes locally
Create Flask app in Container
Run via kubectl

Other Libraries Needed

Docker
Hadolint
Virtualbox
Kubernetes (Minikube)

myersstephen/ml-microservice-container-kubernetes