From 622d3d8046c43ac3a5421d5b3e3c9dcad4c1aa6a Mon Sep 17 00:00:00 2001
From: harvard-edge <khoshnevis.naeem@gmail.com>
Date: Mon, 2 Sep 2024 10:39:01 +0000
Subject: [PATCH] Push dev branch build

---
 docs/contents/contributors.html          | 14 +++++++-------
 docs/contents/ml_systems/ml_systems.html |  4 ++--
 docs/index.html                          |  4 ++--
 docs/search.json                         |  4 ++--
 4 files changed, 13 insertions(+), 13 deletions(-)

diff --git a/docs/contents/contributors.html b/docs/contents/contributors.html
index 6f6227fa..bbf4f560 100644
--- a/docs/contents/contributors.html
+++ b/docs/contents/contributors.html
@@ -744,10 +744,10 @@ <h1 class="title">Contributors &amp; Thanks</h1>
 </tr>
 <tr>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/shanzehbatool"><img src="https://avatars.githubusercontent.com/shanzehbatool?s=100" width="100px;" alt="shanzehbatool"><br><sub><b>shanzehbatool</b></sub></a><br>
+<a href="https://github.com/Mjrovai"><img src="https://avatars.githubusercontent.com/Mjrovai?s=100" width="100px;" alt="Marcelo Rovai"><br><sub><b>Marcelo Rovai</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/Mjrovai"><img src="https://avatars.githubusercontent.com/Mjrovai?s=100" width="100px;" alt="Marcelo Rovai"><br><sub><b>Marcelo Rovai</b></sub></a><br>
+<a href="https://github.com/shanzehbatool"><img src="https://avatars.githubusercontent.com/shanzehbatool?s=100" width="100px;" alt="shanzehbatool"><br><sub><b>shanzehbatool</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
 <a href="https://github.com/mpstewart1"><img src="https://avatars.githubusercontent.com/mpstewart1?s=100" width="100px;" alt="Matthew Stewart"><br><sub><b>Matthew Stewart</b></sub></a><br>
@@ -832,16 +832,16 @@ <h1 class="title">Contributors &amp; Thanks</h1>
 <a href="https://github.com/jared-ni"><img src="https://avatars.githubusercontent.com/jared-ni?s=100" width="100px;" alt="Jared Ni"><br><sub><b>Jared Ni</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/Ekhao"><img src="https://avatars.githubusercontent.com/Ekhao?s=100" width="100px;" alt="Emil Njor"><br><sub><b>Emil Njor</b></sub></a><br>
+<a href="https://github.com/oishib"><img src="https://avatars.githubusercontent.com/oishib?s=100" width="100px;" alt="oishib"><br><sub><b>oishib</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/oishib"><img src="https://avatars.githubusercontent.com/oishib?s=100" width="100px;" alt="oishib"><br><sub><b>oishib</b></sub></a><br>
+<a href="https://github.com/harvard-edge/cs249r_book/graphs/contributors"><img src="https://www.gravatar.com/avatar/71b1bd9ab89c2648e3a1c9641d5e0c54?d=identicon&amp;s=100?s=100" width="100px;" alt="Haoran Qiu"><br><sub><b>Haoran Qiu</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
 <a href="https://github.com/MichaelSchnebly"><img src="https://avatars.githubusercontent.com/MichaelSchnebly?s=100" width="100px;" alt="Michael Schnebly"><br><sub><b>Michael Schnebly</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/leo47007"><img src="https://avatars.githubusercontent.com/leo47007?s=100" width="100px;" alt="Yu-Shun Hsiao"><br><sub><b>Yu-Shun Hsiao</b></sub></a><br>
+<a href="https://github.com/Ekhao"><img src="https://avatars.githubusercontent.com/Ekhao?s=100" width="100px;" alt="Emil Njor"><br><sub><b>Emil Njor</b></sub></a><br>
 </td>
 </tr>
 <tr>
@@ -849,13 +849,13 @@ <h1 class="title">Contributors &amp; Thanks</h1>
 <a href="https://github.com/BaeHenryS"><img src="https://avatars.githubusercontent.com/BaeHenryS?s=100" width="100px;" alt="Henry Bae"><br><sub><b>Henry Bae</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/harvard-edge/cs249r_book/graphs/contributors"><img src="https://www.gravatar.com/avatar/71b1bd9ab89c2648e3a1c9641d5e0c54?d=identicon&amp;s=100?s=100" width="100px;" alt="Haoran Qiu"><br><sub><b>Haoran Qiu</b></sub></a><br>
+<a href="https://github.com/mmaz"><img src="https://avatars.githubusercontent.com/mmaz?s=100" width="100px;" alt="Mark Mazumder"><br><sub><b>Mark Mazumder</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
 <a href="https://github.com/jaywonchung"><img src="https://avatars.githubusercontent.com/jaywonchung?s=100" width="100px;" alt="Jae-Won Chung"><br><sub><b>Jae-Won Chung</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
-<a href="https://github.com/mmaz"><img src="https://avatars.githubusercontent.com/mmaz?s=100" width="100px;" alt="Mark Mazumder"><br><sub><b>Mark Mazumder</b></sub></a><br>
+<a href="https://github.com/leo47007"><img src="https://avatars.githubusercontent.com/leo47007?s=100" width="100px;" alt="Yu-Shun Hsiao"><br><sub><b>Yu-Shun Hsiao</b></sub></a><br>
 </td>
 <td align="center" valign="top" width="20%">
 <a href="https://github.com/marcozennaro"><img src="https://avatars.githubusercontent.com/marcozennaro?s=100" width="100px;" alt="Marco Zennaro"><br><sub><b>Marco Zennaro</b></sub></a><br>
diff --git a/docs/contents/ml_systems/ml_systems.html b/docs/contents/ml_systems/ml_systems.html
index 84ce98fc..e64e6230 100644
--- a/docs/contents/ml_systems/ml_systems.html
+++ b/docs/contents/ml_systems/ml_systems.html
@@ -881,13 +881,13 @@ <h3 data-number="2.3.3" class="anchored" data-anchor-id="challenges-1"><span cla
 </section>
 <section id="example-use-cases-1" class="level3" data-number="2.3.4">
 <h3 data-number="2.3.4" class="anchored" data-anchor-id="example-use-cases-1"><span class="header-section-number">2.3.4</span> Example Use Cases</h3>
-<p>Edge ML has many applications, from autonomous vehicles and smart homes to industrial IoT. These examples were chosen to highlight scenarios where real-time data processing, reduced latency, and enhanced privacy are not just beneficial but often critical to the operation and success of these technologies. They demonstrate the role that Edge ML can play in driving advancements in various sectors, fostering innovation, and paving the way for more intelligent, responsive, and adaptive systems.</p>
+<p>Edge ML has many applications, from autonomous vehicles and smart homes to industrial Internet of Things (IoT). These examples were chosen to highlight scenarios where real-time data processing, reduced latency, and enhanced privacy are not just beneficial but often critical to the operation and success of these technologies. They demonstrate the role that Edge ML can play in driving advancements in various sectors, fostering innovation, and paving the way for more intelligent, responsive, and adaptive systems.</p>
 <p><strong>Autonomous Vehicles</strong></p>
 <p>Autonomous vehicles stand as a prime example of Edge ML’s potential. These vehicles rely heavily on real-time data processing to navigate and make decisions. Localized machine learning models assist in quickly analyzing data from various sensors to make immediate driving decisions, ensuring safety and smooth operation.</p>
 <p><strong>Smart Homes and Buildings</strong></p>
 <p>Edge ML plays a crucial role in efficiently managing various systems in smart homes and buildings, from lighting and heating to security. By processing data locally, these systems can operate more responsively and harmoniously with the occupants’ habits and preferences, creating a more comfortable living environment.</p>
 <p><strong>Industrial IoT</strong></p>
-<p>The Industrial Internet of Things (IoT) leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency.</p>
+<p>The Industrial IoT leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency is transforming manufacturing and production across various sectors.</p>
 <p>The applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems.</p>
 </section>
 </section>
diff --git a/docs/index.html b/docs/index.html
index 876cb308..093bc650 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -7,7 +7,7 @@
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 
 <meta name="author" content="Vijay Janapa Reddi">
-<meta name="dcterms.date" content="2024-09-01">
+<meta name="dcterms.date" content="2024-09-02">
 
 <title>Machine Learning Systems</title>
 <style>
@@ -683,7 +683,7 @@ <h1 class="title">Machine Learning Systems</h1>
     <div>
     <div class="quarto-title-meta-heading">Last Updated</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">September 1, 2024</p>
+      <p class="date">September 2, 2024</p>
     </div>
   </div>
   
diff --git a/docs/search.json b/docs/search.json
index 2af00740..e8387264 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -59,7 +59,7 @@
     "href": "contents/contributors.html",
     "title": "Contributors & Thanks",
     "section": "",
-    "text": "We extend our sincere thanks to the diverse group of individuals who have generously contributed their expertise, insights, time, and support to improve both the content and codebase of this project. This includes not only those who have directly contributed through code and writing but also those who have helped by identifying issues, providing feedback, and offering suggestions. Below, you will find a list of all contributors. If you would like to contribute to this project, please visit our GitHub page for more information.\n\n\n\n\n\n\n\n\nVijay Janapa Reddi\n\n\nIkechukwu Uchendu\n\n\nNaeem Khoshnevis\n\n\nDouwe den Blanken\n\n\njasonjabbour\n\n\n\n\nshanzehbatool\n\n\nMarcelo Rovai\n\n\nMatthew Stewart\n\n\nElias Nuwara\n\n\nkai4avaya\n\n\n\n\nJared Ping\n\n\nItai Shapira\n\n\nMaximilian Lam\n\n\nJayson Lin\n\n\nAndrea\n\n\n\n\nJeffrey Ma\n\n\nSophia Cho\n\n\nAlex Rodriguez\n\n\nKorneel Van den Berghe\n\n\nColby Banbury\n\n\n\n\nZishen Wan\n\n\nSara Khosravi\n\n\nDivya Amirtharaj\n\n\nAbdulrahman Mahmoud\n\n\nSrivatsan Krishnan\n\n\n\n\nAghyad Deeb\n\n\nEmeka Ezike\n\n\narnaumarin\n\n\nAditi Raju\n\n\nELSuitorHarvard\n\n\n\n\nJared Ni\n\n\nEmil Njor\n\n\noishib\n\n\nMichael Schnebly\n\n\nYu-Shun Hsiao\n\n\n\n\nHenry Bae\n\n\nHaoran Qiu\n\n\nJae-Won Chung\n\n\nMark Mazumder\n\n\nMarco Zennaro\n\n\n\n\neurashin\n\n\nAndrew Bass\n\n\nPong Trairatvorakul\n\n\nJennifer Zhou\n\n\nShvetank Prakash\n\n\n\n\nAlex Oesterling\n\n\nAllen-Kuang\n\n\nBruno Scaglione\n\n\ngnodipac886\n\n\nGauri Jain\n\n\n\n\nFin Amin\n\n\nSercan Aygün\n\n\nBaldassarre Cesarano\n\n\nYang Zhou\n\n\nabigailswallow\n\n\n\n\nyanjingl\n\n\nJason Yik\n\n\nhappyappledog\n\n\nCurren Iyer\n\n\nEmmanuel Rassou\n\n\n\n\nShreya Johri\n\n\nJessica Quaye\n\n\nThe Random DIY\n\n\nSonia Murthy\n\n\nVijay Edupuganti\n\n\n\n\nCostin-Andrei Oncescu\n\n\nAnnie Laurie Cook\n\n\nJothi Ramaswamy\n\n\nBatur Arslan\n\n\na-saraf\n\n\n\n\nsonghan\n\n\nZishen",
+    "text": "We extend our sincere thanks to the diverse group of individuals who have generously contributed their expertise, insights, time, and support to improve both the content and codebase of this project. This includes not only those who have directly contributed through code and writing but also those who have helped by identifying issues, providing feedback, and offering suggestions. Below, you will find a list of all contributors. If you would like to contribute to this project, please visit our GitHub page for more information.\n\n\n\n\n\n\n\n\nVijay Janapa Reddi\n\n\nIkechukwu Uchendu\n\n\nNaeem Khoshnevis\n\n\nDouwe den Blanken\n\n\njasonjabbour\n\n\n\n\nMarcelo Rovai\n\n\nshanzehbatool\n\n\nMatthew Stewart\n\n\nElias Nuwara\n\n\nkai4avaya\n\n\n\n\nJared Ping\n\n\nItai Shapira\n\n\nMaximilian Lam\n\n\nJayson Lin\n\n\nAndrea\n\n\n\n\nJeffrey Ma\n\n\nSophia Cho\n\n\nAlex Rodriguez\n\n\nKorneel Van den Berghe\n\n\nColby Banbury\n\n\n\n\nZishen Wan\n\n\nSara Khosravi\n\n\nDivya Amirtharaj\n\n\nAbdulrahman Mahmoud\n\n\nSrivatsan Krishnan\n\n\n\n\nAghyad Deeb\n\n\nEmeka Ezike\n\n\narnaumarin\n\n\nAditi Raju\n\n\nELSuitorHarvard\n\n\n\n\nJared Ni\n\n\noishib\n\n\nHaoran Qiu\n\n\nMichael Schnebly\n\n\nEmil Njor\n\n\n\n\nHenry Bae\n\n\nMark Mazumder\n\n\nJae-Won Chung\n\n\nYu-Shun Hsiao\n\n\nMarco Zennaro\n\n\n\n\neurashin\n\n\nAndrew Bass\n\n\nPong Trairatvorakul\n\n\nJennifer Zhou\n\n\nShvetank Prakash\n\n\n\n\nAlex Oesterling\n\n\nAllen-Kuang\n\n\nBruno Scaglione\n\n\ngnodipac886\n\n\nGauri Jain\n\n\n\n\nFin Amin\n\n\nSercan Aygün\n\n\nBaldassarre Cesarano\n\n\nYang Zhou\n\n\nabigailswallow\n\n\n\n\nyanjingl\n\n\nJason Yik\n\n\nhappyappledog\n\n\nCurren Iyer\n\n\nEmmanuel Rassou\n\n\n\n\nShreya Johri\n\n\nJessica Quaye\n\n\nThe Random DIY\n\n\nSonia Murthy\n\n\nVijay Edupuganti\n\n\n\n\nCostin-Andrei Oncescu\n\n\nAnnie Laurie Cook\n\n\nJothi Ramaswamy\n\n\nBatur Arslan\n\n\na-saraf\n\n\n\n\nsonghan\n\n\nZishen",
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@@ -235,7 +235,7 @@
     "href": "contents/ml_systems/ml_systems.html#edge-ml",
     "title": "2  ML Systems",
     "section": "2.3 Edge ML",
-    "text": "2.3 Edge ML\n\n2.3.1 Characteristics\nDefinition of Edge ML\nEdge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach aims to bring computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy.\nDecentralized Data Processing\nIn Edge ML, data processing happens in a decentralized fashion. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices (Figure 2.4). This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server’s resources. This decentralization is particularly important in real-time applications where even a slight delay can have significant consequences.\nLocal Data Storage and Computation\nLocal data storage and computation are key features of Edge ML. This setup ensures that data can be stored and analyzed directly on the devices, thereby maintaining the privacy of the data and reducing the need for constant internet connectivity. Moreover, this often leads to more efficient computation, as data doesn’t have to travel long distances, and computations are performed with a more nuanced understanding of the local context, which can sometimes result in more insightful analyses.\n\n\n\n\n\n\nFigure 2.4: Edge ML Examples. Source: Edge Impulse.\n\n\n\n\n\n2.3.2 Benefits\nReduced Latency\nOne of Edge ML’s main advantages is the significant latency reduction compared to Cloud ML. This reduced latency can be a critical benefit in situations where milliseconds count, such as in autonomous vehicles, where quick decision-making can mean the difference between safety and an accident.\nEnhanced Data Privacy\nEdge ML also offers improved data privacy, as data is primarily stored and processed locally. This minimizes the risk of data breaches that are more common in centralized data storage solutions. Sensitive information can be kept more secure, as it’s not sent over networks that could be intercepted.\nLower Bandwidth Usage\nOperating closer to the data source means less data must be sent over networks, reducing bandwidth usage. This can result in cost savings and efficiency gains, especially in environments where bandwidth is limited or costly.\n\n\n2.3.3 Challenges\nLimited Computational Resources Compared to Cloud ML\nHowever, Edge ML has its challenges. One of the main concerns is the limited computational resources compared to cloud-based solutions. Endpoint devices may have a different processing power or storage capacity than cloud servers, limiting the complexity of the machine learning models that can be deployed.\nComplexity in Managing Edge Nodes\nManaging a network of edge nodes can introduce complexity, especially regarding coordination, updates, and maintenance. Ensuring all nodes operate seamlessly and are up-to-date with the latest algorithms and security protocols can be a logistical challenge.\nSecurity Concerns at the Edge Nodes\nWhile Edge ML offers enhanced data privacy, edge nodes can sometimes be more vulnerable to physical and cyber-attacks. Developing robust security protocols that protect data at each node without compromising the system’s efficiency remains a significant challenge in deploying Edge ML solutions.\n\n\n2.3.4 Example Use Cases\nEdge ML has many applications, from autonomous vehicles and smart homes to industrial IoT. These examples were chosen to highlight scenarios where real-time data processing, reduced latency, and enhanced privacy are not just beneficial but often critical to the operation and success of these technologies. They demonstrate the role that Edge ML can play in driving advancements in various sectors, fostering innovation, and paving the way for more intelligent, responsive, and adaptive systems.\nAutonomous Vehicles\nAutonomous vehicles stand as a prime example of Edge ML’s potential. These vehicles rely heavily on real-time data processing to navigate and make decisions. Localized machine learning models assist in quickly analyzing data from various sensors to make immediate driving decisions, ensuring safety and smooth operation.\nSmart Homes and Buildings\nEdge ML plays a crucial role in efficiently managing various systems in smart homes and buildings, from lighting and heating to security. By processing data locally, these systems can operate more responsively and harmoniously with the occupants’ habits and preferences, creating a more comfortable living environment.\nIndustrial IoT\nThe Industrial Internet of Things (IoT) leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency.\nThe applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems.",
+    "text": "2.3 Edge ML\n\n2.3.1 Characteristics\nDefinition of Edge ML\nEdge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach aims to bring computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy.\nDecentralized Data Processing\nIn Edge ML, data processing happens in a decentralized fashion. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices (Figure 2.4). This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server’s resources. This decentralization is particularly important in real-time applications where even a slight delay can have significant consequences.\nLocal Data Storage and Computation\nLocal data storage and computation are key features of Edge ML. This setup ensures that data can be stored and analyzed directly on the devices, thereby maintaining the privacy of the data and reducing the need for constant internet connectivity. Moreover, this often leads to more efficient computation, as data doesn’t have to travel long distances, and computations are performed with a more nuanced understanding of the local context, which can sometimes result in more insightful analyses.\n\n\n\n\n\n\nFigure 2.4: Edge ML Examples. Source: Edge Impulse.\n\n\n\n\n\n2.3.2 Benefits\nReduced Latency\nOne of Edge ML’s main advantages is the significant latency reduction compared to Cloud ML. This reduced latency can be a critical benefit in situations where milliseconds count, such as in autonomous vehicles, where quick decision-making can mean the difference between safety and an accident.\nEnhanced Data Privacy\nEdge ML also offers improved data privacy, as data is primarily stored and processed locally. This minimizes the risk of data breaches that are more common in centralized data storage solutions. Sensitive information can be kept more secure, as it’s not sent over networks that could be intercepted.\nLower Bandwidth Usage\nOperating closer to the data source means less data must be sent over networks, reducing bandwidth usage. This can result in cost savings and efficiency gains, especially in environments where bandwidth is limited or costly.\n\n\n2.3.3 Challenges\nLimited Computational Resources Compared to Cloud ML\nHowever, Edge ML has its challenges. One of the main concerns is the limited computational resources compared to cloud-based solutions. Endpoint devices may have a different processing power or storage capacity than cloud servers, limiting the complexity of the machine learning models that can be deployed.\nComplexity in Managing Edge Nodes\nManaging a network of edge nodes can introduce complexity, especially regarding coordination, updates, and maintenance. Ensuring all nodes operate seamlessly and are up-to-date with the latest algorithms and security protocols can be a logistical challenge.\nSecurity Concerns at the Edge Nodes\nWhile Edge ML offers enhanced data privacy, edge nodes can sometimes be more vulnerable to physical and cyber-attacks. Developing robust security protocols that protect data at each node without compromising the system’s efficiency remains a significant challenge in deploying Edge ML solutions.\n\n\n2.3.4 Example Use Cases\nEdge ML has many applications, from autonomous vehicles and smart homes to industrial Internet of Things (IoT). These examples were chosen to highlight scenarios where real-time data processing, reduced latency, and enhanced privacy are not just beneficial but often critical to the operation and success of these technologies. They demonstrate the role that Edge ML can play in driving advancements in various sectors, fostering innovation, and paving the way for more intelligent, responsive, and adaptive systems.\nAutonomous Vehicles\nAutonomous vehicles stand as a prime example of Edge ML’s potential. These vehicles rely heavily on real-time data processing to navigate and make decisions. Localized machine learning models assist in quickly analyzing data from various sensors to make immediate driving decisions, ensuring safety and smooth operation.\nSmart Homes and Buildings\nEdge ML plays a crucial role in efficiently managing various systems in smart homes and buildings, from lighting and heating to security. By processing data locally, these systems can operate more responsively and harmoniously with the occupants’ habits and preferences, creating a more comfortable living environment.\nIndustrial IoT\nThe Industrial IoT leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency is transforming manufacturing and production across various sectors.\nThe applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems.",
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       "Fundamentals",
       "<span class='chapter-number'>2</span>  <span class='chapter-title'>ML Systems</span>"