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docs/contents/frameworks/frameworks.html

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 <h2 data-number="6.9" class="anchored" data-anchor-id="choosing-the-right-framework"><span class="header-section-number">6.9</span> Choosing the Right Framework</h2>
 <p>Choosing the right machine learning framework for a given application requires carefully evaluating models, hardware, and software considerations. By analyzing these three aspects—models, hardware, and software—ML engineers can select the optimal framework and customize it as needed for efficient and performant on-device ML applications. The goal is to balance model complexity, hardware limitations, and software integration to design a tailored ML pipeline for embedded and edge devices.</p>
-<div id="fig-tf-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="TensorFlow Framework Comparison - General">
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-<div id="fig-tf-sw-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Model">
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 <h3 data-number="6.9.3" class="anchored" data-anchor-id="hardware"><span class="header-section-number">6.9.3</span> Hardware</h3>
-<div id="fig-tf-hw-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware">
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 <h3 data-number="6.10.1" class="anchored" data-anchor-id="decomposition"><span class="header-section-number">6.10.1</span> Decomposition</h3>
 <p>Currently, the ML system stack consists of four abstractions as shown in <a href="#fig-mlsys-stack" class="quarto-xref">Figure&nbsp;<span>6.10</span></a>, namely (1) computational graphs, (2) tensor programs, (3) libraries and runtimes, and (4) hardware primitives.</p>
-<div id="fig-mlsys-stack" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="Four Abstractions in Current ML System Stack">
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docs/contents/introduction/introduction.html

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-<figcaption><em>DALL·E 3 Prompt: A detailed, rectangular, flat 2D illustration depicting a roadmap of a book’s chapters on machine learning systems, set on a crisp clean white background. The image features a winding road traveling through various symbolic landmarks. Each landmark represents a chapter topic: Introduction, ML Systems, Deep Learning, AI Workflow, Data Engineering, AI Frameworks, AI Training, Efficient AI, Model Optimizations, AI Acceleration, Benchmarking AI, On-Device Learning, Embedded AIOps, Security &amp; Privacy, Responsible AI, Sustainable AI, AI for Good, Robust AI, Generative AI. The style is clean, modern, and flat, suitable for a technical book, with each landmark clearly labeled with its chapter title.</em></figcaption>
+<figcaption><em>DALL·E 3 Prompt: A detailed, rectangular, flat 2D illustration depicting a roadmap of a book’s chapters on machine learning systems, set on a crisp, clean white background. The image features a winding road traveling through various symbolic landmarks. Each landmark represents a chapter topic: Introduction, ML Systems, Deep Learning, AI Workflow, Data Engineering, AI Frameworks, AI Training, Efficient AI, Model Optimizations, AI Acceleration, Benchmarking AI, On-Device Learning, Embedded AIOps, Security &amp; Privacy, Responsible AI, Sustainable AI, AI for Good, Robust AI, Generative AI. The style is clean, modern, and flat, suitable for a technical book, with each landmark clearly labeled with its chapter title.</em></figcaption>
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-<p>In the early 1990s, <a href="https://en.wikipedia.org/wiki/Mark_Weiser">Mark Weiser</a>, a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. He envisioned a future where computing would be so seamlessly integrated into our environments that it would become an invisible, integral part of daily life. This vision, which he termed “ubiquitous computing,” promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser’s vision, thanks to the advent and proliferation of machine learning systems.</p>
-<p>Ubiquitous computing <span class="citation" data-cites="weiser1991computer">(<a href="../../references.html#ref-weiser1991computer" role="doc-biblioref">Weiser 1991</a>)</span>, as Weiser imagined, is not merely about embedding processors in everyday objects; it is about imbuing our environment with a form of intelligence that anticipates our needs and acts on our behalf, enhancing our experiences without our explicit command. The key to this ubiquitous intelligence lies in developing and deploying machine learning systems at the edge of our networks.</p>
+<p>In the early 1990s, <a href="https://en.wikipedia.org/wiki/Mark_Weiser">Mark Weiser</a>, a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed “ubiquitous computing,” promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser’s vision, thanks to the advent and proliferation of machine learning systems.</p>
+<p>The concept of ubiquitous computing <span class="citation" data-cites="weiser1991computer">(<a href="../../references.html#ref-weiser1991computer" role="doc-biblioref">Weiser 1991</a>)</span>, as envisioned by Weiser, involves more than just incorporating processors into ordinary objects. It revolves around infusing our surroundings with a kind of intelligence that can predict our requirements and take action on our behalf, enhancing our experiences without us having to issue explicit commands. The crucial element of this pervasive intelligence is developing and implementing machine learning systems at the edge of our networks.</p>
 <div class="no-row-height column-margin column-container"><div id="ref-weiser1991computer" class="csl-entry" role="listitem">
 Weiser, Mark. 1991. <span>“The Computer for the 21st Century.”</span> <em>Sci. Am.</em> 265 (3): 94–104. <a href="https://doi.org/10.1038/scientificamerican0991-94">https://doi.org/10.1038/scientificamerican0991-94</a>.
-</div></div><p>Machine learning, a subset of artificial intelligence, enables computers to learn from and make decisions based on data rather than following explicitly programmed instructions. When deployed at the edge—closer to where data is generated, and actions are taken—machine learning systems can process information in real-time, responding to environmental changes and user inputs with minimal latency. This capability is critical for applications where timing is crucial, such as autonomous vehicles, real-time language translation, and smart healthcare devices.</p>
+</div></div><p>Machine learning, a subset of artificial intelligence, enables computers to learn from and make decisions based on data rather than following explicitly programmed instructions. When deployed at the edge, closer to data generation and action, these systems can process information in real time with minimal latency. This is critical for TinyML applications, where fast response is crucial, such as autonomous vehicles, real-time translation, and smart healthcare devices.</p>
 <p>The migration of machine learning from centralized data centers to the edge of networks marks a significant evolution in computing architecture. The need for speed, privacy, and reduced bandwidth consumption drives this shift. By processing data locally, edge-based machine learning systems can make quick decisions without constantly communicating with a central server. This speeds up response times, conserves bandwidth, and enhances privacy by limiting the amount of data transmitted over the network.</p>
 <p>Moreover, the ability to deploy machine learning models in diverse environments has led to an explosion of innovative applications. From smart cities that optimize traffic flow in real-time to agricultural drones that monitor crop health and apply treatments precisely where needed, machine learning at the edge enables a level of contextual awareness and responsiveness that was previously unimaginable.</p>
 <p>Despite the promise of ubiquitous intelligence, deploying machine learning systems at the edge is challenging. These systems must operate within the constraints of limited processing power, memory, and energy availability, often in environments far from the controlled conditions of data centers. Additionally, ensuring the privacy and security of the data in these systems processes is paramount, particularly in applications that handle sensitive personal information.</p>