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.
This project provides comprehensive instructions and template code for taking a trained machine learning model and making it accessible to end-users. It guides you through turning your model into a web application, a mobile app, or an API endpoint. Data scientists and machine learning engineers who need to move their models from development to production will find this repository useful.
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Use this if you have a machine learning model ready and need practical guidance and code examples to deploy it as an application or service for others to use.
Not ideal if you are looking for help with training or evaluating machine learning models, as this project focuses solely on deployment.
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Last pushed
Feb 15, 2023
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