Welcome to the SuperDataScience Community Project!

Welcome to the ModelOps: Deploying Machine Learning Models to Production repository! 🎉

This project is a collaborative initiative brought to you by SuperDataScience, a global community dedicated to advancing the fields of Data Science, Machine Learning, and AI. We’re excited to have you on board for this journey of hands-on learning, experimentation, and growth.

To contribute to this project, please follow the guidelines in our CONTRIBUTING.md.

📂 Repository Structure

This project supports two tracks based on experience level:

SDS-CP040-modelops/
├── beginner/                 ← Beginner track files
│   ├── README.md             ← Scope of Works for Beginner Track
│   ├── REPORT.md             ← Markdown template for beginner submissions
│   └── submissions/
│       ├── team-members/
│       └── community-contributions/
│
├── advanced/                 ← Advanced track files
│   ├── README.md             ← Scope of Works for Advanced Track
│   ├── REPORT.md             ← Markdown template for advanced submissions
│   └── submissions/
│       ├── team-members/
│       └── community-contributions/
│
├── CONTRIBUTING.md
├── requirements.txt
└── README.md                 ← You are here!

🟢 Beginner Track

The Beginner Track introduces participants to core MLOps fundamentals with a simple, hands-on deployment flow. You’ll:

• Build a Streamlit or Gradio UI around a ready-made ML model
• Containerize the app with Docker
• Deploy it to Hugging Face Spaces for a live, shareable demo

📌 Get started: ➡️ Beginner Track Scope of Works ➡️ Beginner Report Template ➡️ Submit your work

🔴 Advanced Track

The Advanced Track focuses on building a more production-grade ML service. You’ll:

• Develop a FastAPI backend (with a minimal frontend)
• Containerize your application with Docker
• Set up a basic CI/CD pipeline using GitHub Actions
• Deploy the service to a cloud platform such as Hugging Face Spaces, Render, or AWS/GCP

📌 Get started: ➡️ Advanced Track Scope of Works ➡️ Advanced Report Template ➡️ Submit your work

📊 Dataset / Model

For this project, we’ll provide pre-trained ML model artifacts that already include preprocessing and the trained estimator. This ensures participants can focus on serving, containerization, and deployment rather than model training.

🗂️ Project Workflow & Timeline

Week	Beginner Track (UI-first)	Advanced Track (API-first)
Week 1	Setup + Build Streamlit/Gradio UI + Local test	Setup + FastAPI service + Local inference
Week 2	Containerize app & deploy to Huggingface spaces	Containerize FastAPI app with Docker
Week 3	-	Deploy and setup CI/CD pipelines

🙌 Contributions & Community

This project is open to both official team members and outside community contributors.

• 🧑‍💻 Team Members should submit their work under team-members/
• 🌍 Community Contributors are welcome to fork the repo and submit under community-contributions/

See CONTRIBUTING.md for guidelines on how to participate.