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      🚀 <b>Our mission. 1 ⭐ = 1 👩‍🎓 Learner</b>. Every star tells a story: learners gaining knowledge and supporters fueling our mission. Together, we're making a difference. Thank you for your support and happy holidays!  
      🎓 [Nov 15] The <a href="https://www.edgeaifoundation.org/">EDGE AI Foundation</a> is <b>matching academic scholarship funds</b> for every new GitHub ⭐ (up to 10,000 stars). <a href="https://github.com/harvard-edge/cs249r_book">Click here to show support!</a> 🙏  
      📘 [Dec 22] <b>Chapter 3 updated!</b> New revisions include expanded content and improved explanations. Check it out <a href="https://mlsysbook.ai/contents/core/dl_primer/dl_primer.html">here</a>. 🌟

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  title: "Machine Learning Systems"
  subtitle: "Principles and Practices of Engineering Artificially Intelligent Systems"

  date: today
  date-format: long

  author: 
    name: Vijay Janapa Reddi
    email: vj@eecs.harvard.edu
    url: https://www.google.com/search?q=Vijay+Janapa+Reddi
    affiliations: Harvard University
    corresponding: true
    roles: "Author, editor and curator."
      
  abstract: |
    {{< var title.long >}} offers readers an entry point to understand
    machine learning (ML) systems by grounding concepts in applied ML. As
    the demand for efficient and scalable ML solutions grows, the ability
    to construct robust ML pipelines becomes increasingly crucial. This
    book focuses on demystifying the process of developing complete ML systems
    suitable for deployment, spanning key phases like data collection,
    model design, optimization, acceleration, security hardening, and
    integration, all from a systems perspective. The text covers a wide
    range of concepts relevant to general ML engineering across industries
    and applications, using TinyML as a pedagogical tool due to its global
    accessibility. Readers will learn basic principles around designing ML
    model architectures, hardware-aware training strategies, performant
    inference optimization, and benchmarking methodologies. The book also
    explores crucial systems considerations in areas like reliability,
    privacy, responsible AI, and solution validation. Enjoy reading it!
    
    ---

    🎙 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="notebooklm_podcast_mlsysbookai.mp3" type="audio/mpeg">
    </audio>
    
    _Acknowledgment:_ Special thanks to [Marco Zennaro](https://www.ictp.it/member/marco-zennaro), one of our early community contributors who helped us with the [AI for Good](./contents/core/ai_for_good/ai_for_good.qmd) chapter, for inspiring the creation of this podcast. Thank you, Marco!
    
    ----

  repo-url: https://github.com/harvard-edge/cs249r_book
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      Written, edited and curated by Prof. Vijay Janapa Reddi (Harvard University)
    right: |
      This book was built with <a href="https://quarto.org/">Quarto</a>.
      
  chapters:
    - text: "---"
    - index.qmd
    - contents/core/acknowledgements/acknowledgements.qmd
    - contents/core/about/about.qmd
    - contents/ai/socratiq.qmd
    - text: "---"
    - contents/core/introduction/introduction.qmd
    - contents/core/ml_systems/ml_systems.qmd
    - contents/core/dl_primer/dl_primer.qmd
#    - contents/core/dl_architectures/dl_architectures.qmd
    - 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/ondevice_learning/ondevice_learning.qmd
    - contents/core/ops/ops.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
    - contents/core/generative_ai/generative_ai.qmd
    - contents/core/ai_for_good/ai_for_good.qmd
    - contents/core/conclusion/conclusion.qmd
    - text: "---"  
    - part: contents/labs/labs.qmd
      chapters:
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      - contents/labs/getting_started.qmd
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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/setup/setup.qmd
      - contents/labs/raspi/image_classification/image_classification.qmd
      - contents/labs/raspi/object_detection/object_detection.qmd
      - contents/labs/raspi/llm/llm.qmd
      - contents/labs/raspi/vlm/vlm.qmd
    - part: contents/labs/shared/shared.qmd
      chapters:
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      - contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd
    - text: "---"  
    - part: REFERENCES
      chapters: 
        - references.qmd

bibliography: 
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  - contents/core/ai_for_good/ai_for_good.bib
  - contents/core/benchmarking/benchmarking.bib
  - contents/core/data_engineering/data_engineering.bib
  - contents/core/dl_primer/dl_primer.bib
  - contents/core/efficient_ai/efficient_ai.bib
  - contents/core/ml_systems/ml_systems.bib
  - contents/core/frameworks/frameworks.bib
  - contents/core/generative_ai/generative_ai.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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