Operationalize-a-Machine-Learning-Microservice-API

Microservice Project [Udacity Cloud DevOps Engineer Nanodegree]

Project Overview

Deploy a containerized Python flask application to serve out predictions (inference) about housing prices through API calls. It uses a a pre-trained, sklearn model that has been trained to predict housing prices in Boston according to several features.

Project Procedure

• Test project code using linting
• Complete a Dockerfile to containerize this application
• Deploy containerized application using Docker and make a prediction
• 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 the code has been tested

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Getting Started

Setup the Environment

• Create a virtualenv and activate it

python3 -m venv <your_venv>
source <your_venv>/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