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harvard-edge/cs249r_book

📈 Trending★ 24,207PythonAudience · developerComplexity · 3/5ActiveSetup · moderate

TLDR

Open-access textbook and hands-on curriculum teaching how to build complete, real-world AI systems, not just train models in isolation.

Mindmap

mindmap
  root((repo))
    What it does
      Textbook on ML systems
      Interactive labs
      Hardware deployment
    Learning materials
      TinyTorch framework
      Infrastructure simulator
      AI-guided reading tool
    Topics covered
      Edge ML
      Embedded systems
      Cloud ML
      Deep learning
    Audience
      Students
      Educators
      ML engineers

Things people build with this

USE CASE 1

Learn how to design and optimize machine learning systems for resource-constrained devices like phones and IoT hardware.

USE CASE 2

Build a complete ML framework from scratch using TinyTorch to understand how training and inference actually work under the hood.

USE CASE 3

Deploy trained models on Arduino or Raspberry Pi and debug real-world constraints like memory and power consumption.

USE CASE 4

Teach a university course on ML systems engineering with integrated labs, simulations, and hardware experiments.

Tech stack

PythonJupyterArduinoRaspberry Pi

Getting it running

Difficulty · moderate Time to first run · 30min

Hardware components (Arduino, Raspberry Pi) required for full curriculum; Jupyter notebooks can run without them for initial learning.

License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

This is an open-access textbook and integrated learning curriculum for "Machine Learning Systems," developed at Harvard. It tackles a real gap in AI education: most courses teach you to build AI models in isolation, but few teach you how to engineer complete, reliable AI systems that actually work in the real world. The curriculum is structured as one unified repository with several connected pieces. The textbook (published in two volumes, with a hardcopy edition coming from MIT Press in 2026) provides the core theory and mental models. Interactive labs let students explore design trade-offs hands-on. TinyTorch is a guided project where you build a machine learning framework from scratch, module by module. Hardware kits let you deploy real models on devices like Arduino and Raspberry Pi, confronting actual memory limits and power budgets. An infrastructure simulator lets you reason about large-scale systems without needing to rent expensive cloud resources. There is also an AI-guided reading tool with quizzes and spaced repetition built in. The course targets students and educators who want to move beyond model training and understand how to build efficient, safe, and robust AI systems end-to-end. It covers topics spanning edge machine learning, embedded systems, cloud ML, and deep learning. The material is written in Python and uses Jupyter-style interactive notebooks.

Copy-paste prompts

Prompt 1
I want to learn how to deploy machine learning models on embedded devices like Arduino. Where should I start in this curriculum?
Prompt 2
Show me how to use TinyTorch to build a simple neural network framework from scratch, step by step.
Prompt 3
How do I use the infrastructure simulator to understand trade-offs in large-scale ML systems without renting cloud resources?
Prompt 4
What hardware kits and experiments does this course include for learning about real-world ML constraints?
Prompt 5
I'm teaching an ML course. How can I integrate these interactive labs and hands-on projects into my curriculum?
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