PawanKolhe / Operationalize-a-Machine-Learning-Microservice-API

Operationalize a Machine Learning Microservice API

This Microservice Project is part of the Udacity Cloud DevOps Engineer Nanodegree

Project Overview

There is 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.

Project Tasks

Goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications. Tasks in this project:

• Build a Frontend to accept user input data and produce a prediction
• Test project code using linting
• Complete a Dockerfile to containerize this application
• Deploy 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
• Integrate with CircleCI to indicate that your code has been tested

---

Files Explaination

Dockerfile - Contains commands used to create a docker image
Makefile - Contains useful set of commands to setup environment, run tests and run lints
app.py - Python flask app that returns predictions about housing prices when requested using API calls
make_prediction.sh - Send API request to Flash app running and receives response
run_docker.sh - Script to build and run docker image locally
upload_docker.sh - Script to tag and upload docker image to docker hub
run_kubernetes.sh - Script to setup and run app on kubernetes
.circleci/config.yml: CircleCI configuration file for running the tests
templates/index.html: Frontend of this project where you can send input and receive a prediction
static/: Frontend assets

Intall

• Docker (Requirements: WSL2 / Windows 10 Pro, Enterprise, or Education)
• Virtual Machine (VirtualBox / HyperV / etc.)

Setup the Environment

• Create a virtualenv and activate it

python3 -m venv <name_of_venv>
source <name_of_venv>/bin/activate

• Run make install using bash 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

1. To start a local cluster:
   minikube start

2. To deploy this application in kubernetes:
   ./run_kubernetes.sh

3. When the pod is up and running, make predictions using:
   ./make_prediction.sh

4. Delete the cluster after your done:
   minikube delete