https://github.com/kingabzpro/cicd-for-machine-learning

A beginner's project on automating the training, evaluation, versioning, and deployment of models using GitHub Actions.
https://github.com/kingabzpro/cicd-for-machine-learning

ci-cd cml guide hugging-face machine-learning mlops mlops-workflow

Last synced: 6 months ago
JSON representation

A beginner's project on automating the training, evaluation, versioning, and deployment of models using GitHub Actions.

• Host: GitHub
• URL: https://github.com/kingabzpro/cicd-for-machine-learning
• Owner: kingabzpro
• License: apache-2.0
• Created: 2024-01-31T16:10:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-12-02T14:20:49.000Z (10 months ago)
• Last Synced: 2025-04-23T13:29:04.527Z (6 months ago)
• Topics: ci-cd, cml, guide, hugging-face, machine-learning, mlops, mlops-workflow
• Language: Jupyter Notebook
• Homepage: https://www.datacamp.com/tutorial/ci-cd-for-machine-learning
• Size: 1.15 MB
• Stars: 93
• Watchers: 1
• Forks: 40
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# CICD-for-Machine-Learning

[![CI](https://github.com/kingabzpro/CICD-for-Machine-Learning/actions/workflows/ci.yml/badge.svg)](https://github.com/kingabzpro/CICD-for-Machine-Learning/actions/workflows/ci.yml)

[![Continuous Deployment](https://github.com/kingabzpro/CICD-for-Machine-Learning/actions/workflows/cd.yml/badge.svg)](https://github.com/kingabzpro/CICD-for-Machine-Learning/actions/workflows/cd.yml)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kingabzpro/CICD-for-Machine-Learning/blob/main/notebook.ipynb)

[![DataCamp](https://img.shields.io/badge/Datacamp-05192D?style=for-the-badge&logo=datacamp&logoColor=65FF8F)](https://www.datacamp.com/tutorial/ci-cd-for-machine-learning) [![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-md-dark.svg)](https://huggingface.co/spaces/kingabzpro/Drug-Classification)

Learn how to automate model training, evaluation, versioning, and deployment using GitHub Actions with the easiest MLOps guide available online.

## Project Description

In this project, we will be using scikit-learn pipelines to train our random forest algorithm and build a drug classifier. After training, we will automate the evaluation process using CML. Finally, we will build and deploy the web application to Hugging Face Hub. 

From training to evaluation, the entire process will be automated using GitHub actions. All you have to do is push the code to your GitHub repository, and within two minutes, the model will be updated on Hugging Face with the updated app, model, and results.

> **Follow the tutorial:** https://www.datacamp.com/tutorial/ci-cd-for-machine-learning

## Pipeline

![CICD](./asset/CICD-pipeline.png)

## Results

| Model                  | Accuracy | F1 Score |

|------------------------|----------|----------|

| RandomForestClassifier | 97.0%    | 94.0%    |

![CM](./Results/model_results.png)