Made With ML
A free course teaching how to build, deploy and maintain machine learning systems with code.
- Stars
- 49.9k
- Forks
- 7.8k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 8 months ago
- Type
- Learning resource
What it is
Made With ML is a hands-on course, delivered as a GitHub repository of notebooks and Python scripts, that walks through taking a machine learning model from experiment to production. It covers design, development, deployment and iteration, with an emphasis on software engineering practices like testing, logging and serving. The README says the code is meant to run on a personal laptop or on Anyscale clusters.
- Work through the Jupyter notebook to run the core ML workloads interactively
- Read and run the refactored Python scripts (data, train, evaluate, predict, serve, tune) to see production-style structure
- Use the repository as a template for organizing your own ML project with tests and pre-commit checks
- Follow the course videos and written lessons alongside the code
Great for
- Software engineers who want to learn how ML models get deployed and maintained
- Data scientists who build models but have not shipped them to production
- College graduates preparing for industry ML roles
- Product or leadership staff who want a technical foundation for ML projects
Not ideal for
- People looking only for theory or math, since the README emphasizes practical building over derivations
- Anyone wanting a ready-made production app to use directly, since this is a teaching project
- Beginners with no Python experience, because the course assumes working knowledge of Python and ML concepts
How to get it
- 1
Clone and build from source
You'll need: Python 3.10 (recommended by the README) · Git · Jupyter
Clone the repository
git clone https://github.com/GokuMohandas/Made-With-ML.git .Create and activate a virtual environment (local)
python3 -m venv venv # recommend using Python 3.10Install dependencies (local)
python3 -m pip install -r requirements.txtInstall pre-commit hooks
pre-commit install - 2
Run it
- Clone the repository and set your GITHUB_USERNAME in a .env file
- Set up the environment, either locally with a virtual environment or on an Anyscale Workspace
- Start the notebook with jupyter lab notebooks/madewithml.ipynb
- Explore the scripts in the madewithml folder once the notebook walkthrough makes sense
- 3
Get the most out of it
- Follow the course at madewithml.com alongside the repository for the detailed explanations
- Use the Anyscale option if you want GPU compute and a preconfigured environment, as the README suggests
- Adjust the --num-workers, --cpu-per-worker and --gpu-per-worker arguments to match your hardware, as the README notes
- Study the pre-commit setup and the testing, logging and serving code as reference for your own projects
Downloads
Strengths
- Free and open source under the MIT license, with a large community of learners (the README cites 40K+ subscribers)
- Covers the full lifecycle from experimentation to deployment and iteration in one project
- Code is organized into clear modules, making it easy to see how the pieces connect
Trade-offs
- Setup is more involved than a typical notebook; the README gives separate local and Anyscale instructions and cloud compute is suggested for speed
- Much of the detailed walkthrough lives on the external course site, so the repository alone may not explain everything
- The README is truncated and does not show every step, so some setup details are unclear from the text provided
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| Made-With-ML (this) | 49.9k | Learning resource | intermediate | 8 months ago | Software engineers who want to learn how ML models get deployed and maintained |
| flow | 22.3k | Dev tool | intermediate | 4 days ago | JavaScript developers who want type checking without rewriting their code in another language |
| applied-ml | 30.8k | Curated list | beginner | 2.3 years ago | Engineers planning an ML project who want to see how other companies approached a similar problem |
| wandb | 11.3k | Library | beginner | 4 days ago | Machine learning engineers who want to compare training runs over time |
| nni | 14.4k | Library | intermediate | 2.3 years ago | Researchers comparing hyperparameter tuning algorithms such as TPE, Hyperband, or Bayesian methods |
| annotated_deep_learning_paper_implementations | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.