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MLOps with GitHub Actions

This repository demonstrates how to implement workflows for continuous integration and continuous deployment of a simple machine learning models using GitHub Actions.

Table of Contents

Introduction

MLOps (Machine Learning Operations) is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. This project showcases how to leverage GitHub Actions to automate the MLOps pipeline.

Features

  • Automated training and validation of ML models
  • Continuous integration of the serving layer and deployment workflows
  • Model versioning and tracking
  • Integration with cloud services for model deployment

Getting Started

To get started with this project, clone the repository and set up the necessary dependencies.

git clone https://github.com/yourusername/MLOps-githubactions.git
cd MLOps-githubactions

Prerequisites

  • git
  • a text editor

Installation

No installation is required, everything is done on github actions

Usage

  1. Push your changes to the repository.
  2. Trigger the training workflows tagging with v*
  3. Trigger the serving layer workflow tagging with s*
  4. Update the values.yaml with the versions to deploy (model and serving layer)
  5. Lets ArgoCD sync the changes
  6. Monitor the deployment.

Testing

To test the model you can use a simple curl:

curl -X POST "http://localhost:8080/predict" \
-H "Content-Type: application/json" \
-d '{"text": "Volare.com is amazing!"}'

Test environment

To deploy the model on a local Kubernetes cluster you can use kind.

Then you can deploy ArgoCD following this doc

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