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quarto :
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[1/5] index.qmd
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title: Machine Learning Systems
subtitle: Principles and Practices of Engineering Artificially Intelligent Systems
author:
name: Vijay Janapa Reddi
email: vj@eecs.harvard.edu
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.
&nbsp;
<audio controls>
<source src="contents/frontmatter/media/notebooklm_podcast_mlsysbookai.mp3" type="audio/mpeg">
</audio>
----
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🔍 Quiz config - file pattern: *_quizzes.json
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Output created: _book\Machine-Learning-Systems.pdf
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