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diff --git a/index.html b/index.html
@@ -623,7 +623,7 @@ <h2 id="toc-title">Table of contents</h2>
 <header id="title-block-header" class="quarto-title-block default"><nav class="quarto-page-breadcrumbs quarto-title-breadcrumbs d-none d-lg-block" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="./index.html">FRONT MATTER</a></li><li class="breadcrumb-item"><a href="./index.html">Preface</a></li></ol></nav>
 <div class="quarto-title">
 <h1 class="title">Machine Learning Systems</h1>
-<p class="subtitle lead">with TinyML</p>
+<p class="subtitle lead">with tinyML</p>
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@@ -638,7 +638,7 @@ <h1 class="title">Machine Learning Systems</h1>
 <div>
   <div class="abstract">
     <div class="block-title">Abstract</div>
-    Machine Learning Systems with TinyML offers readers an entry point to understand comprehensive machine learning systems by grounding concepts in accessible TinyML applications. As resource-constrained edge computing sees rapid expansion, the ability to construct efficient ML pipelines grows crucial. This book aims to demystify the process of developing complete ML systems suitable for deployment - spanning key phases like data collection, model design, optimization, acceleration, security hardening, and integration. The text touches on the full breadth of concepts relevant to general ML engineering across industries and applications through the lens of TinyML. Readers will learn basic principles around designing ML model architectures, hardware-aware training strategies, performant inference optimization, benchmarking methodologies and more. Additionally, crucial systems considerations in areas like reliability, privacy, responsible AI, and solution validation are also explored in depth. In summary, the book strives to equip newcomers and professionals alike with integrated knowledge covering full stack ML system development, using easily accessible TinyML applications as the vehicle to impart universal concepts required to unlock production ML.
+    Machine Learning Systems with TinyML 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 aims to demystify 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!
   </div>
 </div>
 
@@ -647,7 +647,7 @@ <h1 class="title">Machine Learning Systems</h1>
 
 
 <p><img src="cover-image-transparent.png" title="Machine Learning Systems" class="quarto-cover-image img-fluid" alt="Cover image."></p>
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 <section id="preface" class="level1 unnumbered">
 <h1 class="unnumbered">Preface</h1>
 <p>Welcome to Machine Learning Systems with TinyML. This book is your gateway to the fast-paced world of AI systems through the lens of embedded systems. It is an extension of the course TinyML from <a href="https://sites.google.com/g.harvard.edu/cs249-tinyml-2023">CS249r</a> at Harvard University.</p>