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    content: 🌟 Help Us Reach 1,000 GitHub Stars! 🌟 For every 25 stars, Arduino and SEEED will donate a NiclaVision or XIAO ESP32S3 for AI education. <a href="https://github.com/harvard-edge/cs249r_book">Click here to ⭐</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: 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!

  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>.
      
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    - part: FRONT MATTER
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      - index.qmd
      - contents/dedication.qmd
      - contents/acknowledgements/acknowledgements.qmd
      - contents/contributors.qmd
      - contents/copyright.qmd
      - contents/about.qmd
    - text: "---"
    - part: MAIN
    - part: Fundamentals
      chapters:
      - contents/introduction/introduction.qmd
      - contents/ml_systems/ml_systems.qmd
      - contents/dl_primer/dl_primer.qmd
    - part: Workflow
      chapters:
      - contents/workflow/workflow.qmd
      - contents/data_engineering/data_engineering.qmd
      - contents/frameworks/frameworks.qmd
    - part: Training
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      - contents/training/training.qmd
      - contents/efficient_ai/efficient_ai.qmd
      - contents/optimizations/optimizations.qmd
      - contents/hw_acceleration/hw_acceleration.qmd
    - part: Deployment
      chapters:
      - contents/benchmarking/benchmarking.qmd
      - contents/ondevice_learning/ondevice_learning.qmd
      - contents/ops/ops.qmd
    - part: Advanced Topics
      chapters:
      - contents/privacy_security/privacy_security.qmd
      - contents/responsible_ai/responsible_ai.qmd
      - contents/sustainable_ai/sustainable_ai.qmd
      - contents/robust_ai/robust_ai.qmd
      - contents/generative_ai/generative_ai.qmd
    - part: Social Impact
      chapters:
      - contents/ai_for_good/ai_for_good.qmd
    - part: Closing
      chapters:
      - contents/conclusion/conclusion.qmd
    - text: "---"  
    - part: LABS
      chapters:
      - contents/labs/labs.qmd
      - contents/labs/getting_started.qmd
    - part: contents/labs/arduino/nicla_vision/nicla_vision.qmd
      chapters:
      - contents/labs/arduino/nicla_vision/setup/setup.qmd
      - 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
      chapters:
      - contents/labs/seeed/xiao_esp32s3/setup/setup.qmd
      - 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
      chapters:
      - 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
    - part: contents/labs/shared/shared.qmd
      chapters:
      - contents/labs/shared/kws_feature_eng/kws_feature_eng.qmd
      - contents/labs/shared/dsp_spectral_features_block/dsp_spectral_features_block.qmd
    - text: "---"  
    - part: REFERENCES
      chapters: 
        - references.qmd
    - text: "---"  
  appendices:
      - contents/tools.qmd
      - contents/zoo_datasets.qmd
      - contents/zoo_models.qmd
      - contents/learning_resources.qmd
      - contents/community.qmd
      - contents/case_studies.qmd

bibliography: 
  # main
  - contents/introduction/introduction.bib
  - contents/ai_for_good/ai_for_good.bib
  - contents/benchmarking/benchmarking.bib
  - contents/data_engineering/data_engineering.bib
  - contents/dl_primer/dl_primer.bib
  - contents/efficient_ai/efficient_ai.bib
  - contents/ml_systems/ml_systems.bib
  - contents/frameworks/frameworks.bib
  - contents/generative_ai/generative_ai.bib
  - contents/hw_acceleration/hw_acceleration.bib
  - contents/ondevice_learning/ondevice_learning.bib
  - contents/ops/ops.bib
  - contents/optimizations/optimizations.bib
  - contents/privacy_security/privacy_security.bib
  - contents/responsible_ai/responsible_ai.bib
  - contents/robust_ai/robust_ai.bib
  - contents/sustainable_ai/sustainable_ai.bib
  - contents/training/training.bib
  - contents/workflow/workflow.bib
  - contents/conclusion/conclusion.bib

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  giscus:
    repo: harvard-edge/cs249r_book

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