/operationalize-ml-service

Operationalize a Machine Learning Microservice API using kubernetes

Primary LanguagePythonMIT LicenseMIT

operationalize-ml-service

Operationalize a Machine Learning Microservice API using kubernetes

CircleCI

Project Overview

The ML model is a pre-trained, sklearn model that predicts 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. The API is a Python flask app 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.

Setup the Environment

  • Create a virtualenv and activate it - make setup
  • Install the necessary dependencies - make install

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