Project Overview

This project showcases the skills acquired in the Udacity Cloud DevOps Nanodegree program to operationalize a Machine Learning Microservice API.

This project used 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. Details about the data source is taken from Kaggle, on the data source site. This project improve my 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

The 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.

• I tested the project code using linting
• I completed a Dockerfile to containerize this application
• I deployed the containerized application using Docker and make a prediction
• I improved the log statements in the source code for this application
• I configured Kubernetes and create a Kubernetes cluster
• I deployed a container using Kubernetes and make a prediction
• I upload a complete Github repo with CircleCI to indicate that my 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.

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Setup the Environment

• Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv.

python3 -m pip install --user virtualenv
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
# 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 minikube start
4. Run kubectl config view
5. 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