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    content: |
        🔔 <b>What's New in the Book?</b></br> 
        📅 <b>[02 Feb 2025]</b> Chapter 12 (Benchmarking AI 📊). Learn how to evaluate AI performance effectively.</br> 
        📅 <b>[28 Jan 2025]</b> Updated Chapter 8 & 9 (AI Training 🏋️‍♀️ & Efficient AI ⚡).</br> 
        📅 <b>[16 Jan 2025]</b> Updated Chapter 6 & 7 (AI Frameworks 🛠️ & Data Engineering 🔢).</br> 
        📅 <b>[03 Jan 2025]</b> Happy New Year 2025! Chapters 1-5 have been expanded and improved, in addition to a brand-new Chapter 4!</br> 
        🗒️ See the full <a href="contents/frontmatter/changelog/changelog.html">Changelog</a>. 
        <div style="height: 1px; background-color: #ccc; margin: 10px 0;"></div> 
        🚀 <b>Our Mission:</b> Empowering the next generation of ML systems engineers --- one GitHub ⭐ at a time.</br> 
        🙏 <b>Thank You for Your Support!</b> Every ⭐ inspires and supports a new learner. Help us reach 10,000!</br> 
        ✍️ <b>Fun Fact:</b> A ⭐ a day keeps Vijay writing all day! → <a href="https://github.com/harvard-edge/cs249r_book">Star the book on GitHub to show your support.</a> 
  
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  title: "Machine Learning Systems"
  subtitle: "Principles and Practices of Engineering Artificially Intelligent Systems"

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  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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    roles: "Author, editor and curator."

  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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      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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    - contents/frontmatter/acknowledgements/acknowledgements.qmd
    - text: "---"
    - contents/frontmatter/ai/socratiq.qmd
    - text: "---"
    - part: "<div class=\"part\">AI Essentials</div>"
    - contents/core/introduction/introduction.qmd
    - contents/core/ml_systems/ml_systems.qmd
    - contents/core/dl_primer/dl_primer.qmd
    - contents/core/dnn_architectures/dnn_architectures.qmd
    - part: "<div class=\"part\">AI Engineering Principles</div>"
    - contents/core/workflow/workflow.qmd
    - contents/core/data_engineering/data_engineering.qmd
    - contents/core/frameworks/frameworks.qmd
    - contents/core/training/training.qmd
    - contents/core/efficient_ai/efficient_ai.qmd
    - contents/core/optimizations/optimizations.qmd
    - contents/core/hw_acceleration/hw_acceleration.qmd
    - contents/core/benchmarking/benchmarking.qmd
    - contents/core/ops/ops.qmd
    - part: "<div class=\"part\">AI Best Practices</div>"
    - contents/core/ondevice_learning/ondevice_learning.qmd
    - contents/core/privacy_security/privacy_security.qmd
    - contents/core/responsible_ai/responsible_ai.qmd
    - contents/core/sustainable_ai/sustainable_ai.qmd
    - contents/core/robust_ai/robust_ai.qmd
    - part: "<div class=\"part\">AI Perspectives</div>"
    - contents/core/ai_for_good/ai_for_good.qmd
    - contents/core/conclusion/conclusion.qmd
    - text: "---"
    - part: "LABS"
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    - part: contents/labs/arduino/nicla_vision/nicla_vision.qmd
      chapters:
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      - contents/labs/arduino/nicla_vision/image_classification/image_classification.qmd
      - contents/labs/arduino/nicla_vision/object_detection/object_detection.qmd
      - contents/labs/arduino/nicla_vision/kws/kws.qmd
      - contents/labs/arduino/nicla_vision/motion_classification/motion_classification.qmd
    - part: contents/labs/seeed/xiao_esp32s3/xiao_esp32s3.qmd
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      - contents/labs/seeed/xiao_esp32s3/image_classification/image_classification.qmd
      - contents/labs/seeed/xiao_esp32s3/object_detection/object_detection.qmd
      - contents/labs/seeed/xiao_esp32s3/kws/kws.qmd
      - contents/labs/seeed/xiao_esp32s3/motion_classification/motion_classification.qmd
    - part: contents/labs/raspi/raspi.qmd
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      - contents/labs/raspi/llm/llm.qmd
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    - text: "---"
    - part: "Appendix"
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    - text: "---"
    - part: "REFERENCES"
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bibliography:
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  - contents/core/benchmarking/benchmarking.bib
  - contents/core/data_engineering/data_engineering.bib
  - contents/core/dl_primer/dl_primer.bib
  - contents/core/dnn_architectures/dnn_architectures.bib
  - contents/core/efficient_ai/efficient_ai.bib
  - contents/core/ml_systems/ml_systems.bib
  - contents/core/frameworks/frameworks.bib
  - contents/core/hw_acceleration/hw_acceleration.bib
  - contents/core/ondevice_learning/ondevice_learning.bib
  - contents/core/ops/ops.bib
  - contents/core/optimizations/optimizations.bib
  - contents/core/privacy_security/privacy_security.bib
  - contents/core/responsible_ai/responsible_ai.bib
  - contents/core/robust_ai/robust_ai.bib
  - contents/core/sustainable_ai/sustainable_ai.bib
  - contents/core/training/training.bib
  - contents/core/workflow/workflow.bib
  - contents/core/conclusion/conclusion.bib

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