/Sklearn-Microservice-Flask-App

Primary LanguagePython

CircleCI

Project Overview

This project will contain all the necessary files and scripts to operationalize a Machine Learning Microservice API. The operationalized 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.

This API is using 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.

Repository Content

  • app.py contains the code for the Python flask app
  • requirements.txt contains a list of Python dependencies needed for the Python flask app
  • make_prediction.sh runs a prediction using the operationalized API
  • run_docker.sh builds a docker image for the Python flask app and runs it
  • upload_docker.sh tags and uploads the created docker image to Docker Hub
  • run_kubernetes.sh fetches the docker image from Docker Hub and runs it with Kubernetes

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 (using image from Docker Hub): ./run_kubernetes.sh