Simple Machine Learning CI/CD Pipeline Project: Automating Model Integration with GitHub Actions

Описание: Learn how to build a Simple Machine Learning CI/CD Pipeline using GitHub Actions!

In this step-by-step tutorial, we automate model training and deployment for a housing price prediction model using a tabular dataset.

Whether you’re a beginner or an experienced developer, this guide will help you:
✅ Understand the basics of Continuous Integration (CI) and Continuous Deployment (CD) in Machine Learning.
✅ Set up GitHub Actions to automatically test, train, and deploy your ML model.
✅ Streamline your workflow so every code push triggers your pipeline.
✅ Work with Python, requirements.txt, and automation scripts for preprocessing & model training

What You’ll Learn:

What is CI/CD in Machine Learning

Setting up a GitHub Actions workflow (.yml) file

Automating ML model training on code push

Deploying changes seamlessly without manual intervention

Tech Stack Used:

Python 3.10

Visual Studio Code Platform

GitHub Actions

Pandas, Scikit-learn

Tabular dataset for housing price prediction from kaggle

-Perfect for MLOps beginners, Devep Engineers, Data Scientists, and Machine Learning Engineers looking to improve their deployment workflow.

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#MachineLearning #CICD #GitHubActions #MLOps #Python #MachineLearningPipeline #ModelDeployment #Automation #HousingPricePrediction #DataScience