/Operationalize-a-Machine-Learning-Microservice-API

Primary LanguagePython

CircleCI

Overview

The objective of this project is to operationalize a Machine Learning Microservice API.

Given 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, This project operationalizes it so that it in a Python flask app so that it serves out predictions through API calls.

You can find the following files in this repository:

  • Makefile: Defines a set of takes to be executed to install, lint, and test the project
  • app.py: contains the code that makes predictions based on input
  • Dockerfile: builds a docker container
  • *.sh: utilty scripts to run the project on docker/kubernetes, make predictions,ud or upload images to Docker

Getting Started

Setup

  • Create and activate a virtual environment
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. Docker: bash run_docker.sh
  3. Kubernetes: bash run_kubernetes.sh

Notes

  • To run this project, make sure you install:

  • Docker

  • Hadolint

  • Kubernetes (Minikube)