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_quarto.yml
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🔔 <b>What's New in the Book?</b><br>
📅 <b>[Mar 25]:</b> Major updates! Ch. 13 (ML Operations ⚙️), Ch. 17–19 (Sustainable AI 🌱, Robust AI 🛡️, and AI for Good 🌍).<br>
📅 <b>[Mar 03]:</b> New! Ch. 10 (AI Acceleration 🚀) & Ch. 12 (Benchmarking AI 📊).<br>
📅 <b>[Feb 02]:</b> Updated Ch. 8 & 9 (AI Training 🏋️ & Efficient AI ⚡).<br>
📅 <b>[Jan 16]:</b> Expanded Ch. 1-7 + brand-new Ch. 4! 🔢🛠️<br>
🗒️ <b>More Updates:</b> <a href="contents/frontmatter/changelog/changelog.html">See the Full Changelog</a>.
<div style="height: 1px; background-color: #ccc; margin: 5px 0;"></div>
🚀 <b>Shaping the Future:</b> Every GitHub ⭐ helps empower learners and expand the global AI engineering community.<br>
🙏 <b>Support the Mission:</b> Your ⭐ helps us keep this resource free, open, and improving for everyone.<br>
✍️ <b>Keep It Growing:</b> A ⭐ a day keeps Vijay writing all day! → <a href="https://github.com/harvard-edge/cs249r_book"><b>Star the book on GitHub</b></a>
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title: "Machine Learning Systems"
subtitle: "Principles and Practices of Engineering Artificially Intelligent Systems"
date: today
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author:
name: Vijay Janapa Reddi
email: [email protected]
url: https://www.google.com/search?q=Vijay+Janapa+Reddi
affiliations: Harvard University
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abstract: |
Machine Learning Systems presents a comprehensive approach to understanding and engineering machine learning (ML). While many resources focus on ML algorithms and model architectures, this book serves as a bridge between theoretical foundations and practical engineering. It emphasizes the systems context that engineers need to master when building AI solutions in the real world. The text progresses from foundational concepts to advanced system design, integrating topics such as data engineering, model optimization, hardware-aware training approaches, and inference acceleration strategies. Throughout the book, readers develop a principled understanding of ML systems engineering, learning to reason about system architectures and address critical challenges in areas including security, privacy, and reliability. While ML applications and tools evolve rapidly, the engineering principles for building ML systems remain largely consistent. This book distills these enduring concepts, making it a resource for anyone seeking to build flexible, efficient, and robust ML systems.
----
🎙 Listen to the **AI Podcast**,
created using Google's Notebook LM and inspired by insights drawn from our
[IEEE education viewpoint paper](https://web.eng.fiu.edu/gaquan/Papers/ESWEEK24Papers/CPS-Proceedings/pdfs/CODES-ISSS/563900a043/563900a043.pdf).
This podcast provides an accessible overview of what this book is all about.
<audio controls>
<source src="contents/frontmatter/media/notebooklm_podcast_mlsysbookai.mp3" type="audio/mpeg">
</audio>
----
repo-url: https://github.com/harvard-edge/cs249r_book
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Written, edited and curated by Prof. Vijay Janapa Reddi (Harvard University)
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This book was built with <a href="https://quarto.org/">Quarto</a>.
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- part: "<div class=\"part\">AI Perspectives</div>"
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- contents/core/conclusion/conclusion.qmd
- text: "---"
- part: "LABS"
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- part: contents/labs/arduino/nicla_vision/nicla_vision.qmd
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- part: contents/labs/raspi/raspi.qmd
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- text: "---"
- part: "Appendix"
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- text: "---"
- part: "REFERENCES"
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- contents/core/ml_systems/ml_systems.bib
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