A hands-on, 2-day workshop for beginners on taking a machine learning model from a notebook to a real, deployed, monitored production system.
| Day | Focus | Duration |
|---|---|---|
| Day 1 | Intro & Your First API — entirely local | 3 hrs |
| Day 2 | Ship It on Render, then the Bigger Picture | 3 hrs |
- Data scientists / ML practitioners who can train a model (sklearn-level) but have never deployed one
- Beginners wanting a practical, no-jargon path from
model.fit()to a live API - Zero prior knowledge of APIs, HTTP, or Docker is assumed
This workshop is deliberately split into two tiers, and it says so out loud at the point each one starts:
- Hands-on (you build every piece of this yourself): packaging a model, a working FastAPI with two endpoints, deploying it live on Render, and watching real requests in a live log.
- Awareness only (named and explained, nothing to build today): containers/Docker, CI/CD, model versioning, feature stores, Champion/Challenger deployments, and the rest of the full MLOps picture — covered in Day 2's closing section so you recognize the terms later.
- Basic Python
- Familiarity with training a simple ML model (e.g., scikit-learn)
- A laptop with admin rights to install software — Docker is not required
Install/create these before the workshop — see resources/setup.md for full instructions.
- Python 3.10+
- Git
- A free GitHub account
- A free Render account (no credit card required)
.
├── day1/ # Day 1: packaging a model, building your first API
│ ├── notebooks/ # Train + export the model
│ ├── api/ # FastAPI app
│ └── README.md # Day 1 session guide
├── day2/ # Day 2: deploy on Render, monitor, then zoom out
│ ├── deployment/ # DEPLOY_GUIDE.md + render.yaml (native Python)
│ ├── monitoring/ # Logging example, read as a demo
│ └── README.md # Day 2 session guide
├── bonus/
│ └── docker/ # Optional: try Docker yourself, after the workshop
├── slides/ # The workshop slide deck (.pptx)
├── datasets/ # Sample datasets used in exercises
└── resources/ # Setup guide, cheatsheet, further reading
git clone https://github.com/<your-username>/getting-started-ml-production-shared.git
cd getting-started-ml-production-shared
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt## 📜 License
The workshop's original teaching materials are licensed under CC BY NC 4.0. You are free to use, adapt, and share the materials for non commercial educational purposes, with appropriate attribution.