Updated abstract

_quarto.yml

@@ -47,7 +47,7 @@ book:
   page-navigation: true
   title: "Machine Learning Systems"
   subtitle: "with TinyML"
-  abstract: "{{< var title.long >}} 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."
+  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 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 universal 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
   repo-branch: dev
@@ -221,7 +221,7 @@ format:
         <script src="/scripts/ai_menu/dist/142.bundle.js" defer></script>
         <script src="/scripts/ai_menu/dist/384.bundle.js" defer></script>
         <script src="/scripts/ai_menu/dist/761.bundle.js" defer></script>
-
+        
     citeproc: true
 
   pdf: