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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Machine Learning School
| Date | Stars |
|---|---|
| 2026-07-31 | 442 |
| 2026-08-02 | 442 |
| 2026-08-06 | 444 |
Today
+2 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Building AI/ML Systems That Don't Suck
"Building AI/ML Systems That Don't Suck" is designed to teach you how to train, evaluate, deploy, and monitor AI and Machine Learning models in production.
In this repository, you'll find the code to build a fully-fledged, end-to-end machine learning system that you can use as a starting point for your own projects.
This repository is part of the [Machine Learning School](https://www.ml.school) program.
## Running in a Development Container
The best way to clone and run the source code from this repository is using a Development Container.
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/svpino/ml.school)
Most Integrated Development Environments support Development Containers. If you have Visual Studio Code and Docker installed, you can click the badge above or [this link](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/svpino/ml.school) to automatically install the [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers), clone the repository into a container volume, and spin up a container.
A Development Container is a Docker container configured as a fully functional development environment isolated from your operating system. You can use your IDE to edit, build, and run the project without spending time setting up your local environment or worrying about altering it.
You can find more information about Development Containers in the [Dev Containers documentation](https://code.visualstudio.com/docs/devcontainers/containers).
After opening the project on IDX, click on the "Machine Learning School" extension on the left activity bar. This extension will allow you to navigate the documentation, run the pipelines, and deploy the model.
**Note:** If you had the "Machine Learning School" extension installed before running the project on a Development Container, you'll need to uninstall it and rebuild the container. The extension must be installed on the container for it to work.
## Running on Firebase Studio
An alternative way to run the project is using Google's Firebase Studio. Clicking the button below will create and configure a development environment you can access directly from your browser:
<a href="https://studio.firebase.google.com/import?url=https%3A%2F%2Fgithub.com%2Fsvpino%2Fml.school">
<picture>
<source
media="(prefers-color-scheme: dark)"
srcset="https://cdn.firebasestudio.dev/btn/open_dark_32.svg">
<source
media="(prefers-color-scheme: light)"
srcset="https://cdn.firebasestudio.dev/btn/open_light_32.svg">
<img
height="32"
alt="Open in Firebase Studio"
src="https://cdn.firebasestudio.dev/btn/open_blue_32.svg">
</picture>
</a>
After opening the project on Firebase Studio, click on the "Machine Learning School" extension on the left activity bar. This extension will allow you to navigate the documentation, run the pipelines, and deploy the model.
*Note:* Firebase Studio is an experimental Google product and it might be unstable at times. If you are planning to take full advantage of this repository, and modify it for your own purposes, running in a Development Container is the best option.
## Running the project locally
If you prefer to run the project on your local environment, you can start by
[forking](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo) the [repository](https://github.com/svpino/ml.school) and [cloning](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo) it on your computer.
You can run the code on any Unix-based operating system (e.g., Ubuntu or macOS). If you are using Windows, insExcerpt of 4,646 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:5c8dbeb9cd6ea963, llm:Repository title and description: 'Machine Learning School' — educational resource for machine learning; Jupyter Notebook language suggests tutorials/notebooks.