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CircleCI

Operationalize-a-Machine-Learning-Microservice-API

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 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

Getting Started

Setup the Environment

  • Create a virtualenv and activate it
  • 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