GitHub - Nneji123/Serving-Machine-Learning-Models

This repository contains instructions, template source code and examples on how to serve/deploy machine learning models using various frameworks and applications such as Docker, Flask, FastAPI, BentoML, Streamlit, MLflow and even code on how to deploy your machine learning model as an android app. The Repository also has code and how-to's for deploying your apps to various cloud platforms(AWS, Heroku, Vercel etc), working with Github actions for CI/CD(Continuous Integration and Continuous Development), TDD(Test driven development) with pytest and other useful information. Before we get into building and deploying our models we'll have to setup our environment. I use'pyenv' for managing different versions of python and pyenv-virtualenv for setting up virtual environments. You can learn how to install pyenv on your operating system by checking out their official github.