#Project

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. 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.

Running the application

# to initialize local virtual envirnoment
python3 -m venv ~/.devops
source ~/.devops/bin/activate

# install libraries for local env
make install

# run app
./run_docker

# upload docker to docker hub
./upload_docker.sh

# to make a sample prediction(docker should be up and running). 
./make_prediction.sh

Files:

app.py: The Flask API
requirements.txt: project library requirments .circleci/config.yml: configuration file that defines the circleCI deployment
model_data/boston_housing_prediction.joblib: pretrained model to be used for predictions
Dockerfile: docker file
Makefile: Commands to install and lint the applicaiton
run_docker.sh: builds and run the docker API app on port 8000
run_kubernetes.sh: Runs the API app as a Kubernetes deployment
make_prediction.sh: sample input to test the API
upload_docker.sh: Tags and uploads the Docker image to Docker Hub

Description of project

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
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.

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

Setup the Environment

Create a virtualenv and activate it
Run make install to install the necessary dependencies

Running `app.py`

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

mpizosdim/udadevops