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docs/contents/frameworks/frameworks.html

@@ -914,7 +914,7 @@ <h3 data-number="6.3.1" class="anchored" data-anchor-id="tf-ecosystem"><span cla
 <li><p><a href="https://developers.googleblog.com/2019/03/introducing-coral-our-platform-for.html">TensorFlow on Edge Devices (Coral)</a>: platform of hardware components and software tools from Google that allows the execution of TensorFlow models on edge devices, leveraging Edge TPUs for acceleration.</p></li>
 <li><p><a href="https://www.tensorflow.org/federated">TensorFlow Federated (TFF)</a>: framework for machine learning and other computations on decentralized data. TFF facilitates federated learning, allowing model training across many devices without centralizing the data.</p></li>
 <li><p><a href="https://www.tensorflow.org/graphics">TensorFlow Graphics</a>: library for using TensorFlow to carry out graphics-related tasks, including 3D shapes and point clouds processing, using deep learning.</p></li>
-<li><p><a href="https://www.tensorflow.org/hub">TensorFlow Hub</a>: repository of reusable machine learning model components to allow developers to reuse pre-trained model components, facilitating transfer learning and model composition</p></li>
+<li><p><a href="https://www.tensorflow.org/hub">TensorFlow Hub</a>: repository of reusable machine learning model components to allow developers to reuse pre-trained model components, facilitating transfer learning and model composition.</p></li>
 <li><p><a href="https://www.tensorflow.org/tfx/guide/serving">TensorFlow Serving</a>: framework designed for serving and deploying machine learning models for inference in production environments. It provides tools for versioning and dynamically updating deployed models without service interruption.</p></li>
 <li><p><a href="https://www.tensorflow.org/tfx">TensorFlow Extended (TFX)</a>: end-to-end platform designed to deploy and manage machine learning pipelines in production settings. TFX encompasses data validation, preprocessing, model training, validation, and serving components.</p></li>
 </ol>
@@ -1098,7 +1098,7 @@ <h3 data-number="6.4.1" class="anchored" data-anchor-id="tensor-data-structures"
 <img src="images/png/color_channels_of_image.png" class="img-fluid figure-img">
 </div>
 <figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-tensor-data-structure-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
-Figure&nbsp;6.4: Visualization of colored image structure that can be easily stored as a 3D Tesnor. Credit: <a href="https://towardsdatascience.com/what-are-tensors-in-machine-learning-5671814646ff">Niklas Lang</a>
+Figure&nbsp;6.4: Visualization of colored image structure that can be easily stored as a 3D Tensor. Credit: <a href="https://towardsdatascience.com/what-are-tensors-in-machine-learning-5671814646ff">Niklas Lang</a>
 </figcaption>
 </figure>
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@@ -1648,7 +1648,7 @@ <h3 data-number="6.8.3" class="anchored" data-anchor-id="library"><span class="h
 <section id="choosing-the-right-framework" class="level2" data-number="6.9">
 <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-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - General">
+<div id="fig-tf-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - General" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-tf-comparison-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <img src="images/png/image4.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - General" class="figure-img">
@@ -1664,7 +1664,7 @@ <h3 data-number="6.9.1" class="anchored" data-anchor-id="model"><span class="hea
 </section>
 <section id="software" class="level3" data-number="6.9.2">
 <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="header-section-number">6.9.2</span> Software</h3>
-<div id="fig-tf-sw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Model">
+<div id="fig-tf-sw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - Model" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
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 <img src="images/png/image5.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - Model" class="figure-img">
@@ -1678,7 +1678,7 @@ <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="
 </section>
 <section id="hardware" class="level3" data-number="6.9.3">
 <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-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware">
+<div id="fig-tf-hw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - Hardware" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-tf-hw-comparison-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <img src="images/png/image3.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware" class="figure-img">
@@ -1724,7 +1724,7 @@ <h2 data-number="6.10" class="anchored" data-anchor-id="future-trends-in-ml-fram
 <section id="decomposition" class="level3" data-number="6.10.1">
 <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>6.11</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-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="Four Abstractions in Current ML System Stack">
+<div id="fig-mlsys-stack" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="Four Abstractions in Current ML System Stack" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-mlsys-stack-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <img src="images/png/image8.png" class="img-fluid figure-img" data-align="center" data-caption="Four Abstractions in Current ML System Stack">

docs/index.html

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 <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="2024-08-11">
+<meta name="dcterms.date" content="2024-08-14">
 
 <title>Machine Learning Systems</title>
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@@ -649,7 +649,7 @@ <h1 class="title">Machine Learning Systems</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">August 11, 2024</p>
+      <p class="date">August 14, 2024</p>
     </div>
   </div>