Updating the intro to be a bit more generic

Learning ML Systems through the lens of tinyML

_quarto.yml

@@ -27,8 +27,10 @@ book:
 
   page-navigation: true
   title: "MACHINE LEARNING SYSTEMS"
-  subtitle: "for TinyML"
-  abstract: Machine Learning Systems for TinyML offers comprehensive guidance on deploying machine learning on embedded devices. As edge computing and the Internet of Things proliferate, this textbook provides professionals and students the expertise to implement performant AI on resource-constrained hardware. A unique aspect of this book elucidates the entire machine learning workflow, from data engineering through training, optimization, acceleration, and production deployment. Key topics covered include deep learning and classical ML algorithms for embedded systems, efficient neural network architectures, hardware-aware training techniques, model compression, benchmarking for tinyML, and on-device learning. Additional chapters highlight cutting-edge advances like on-device data generation and crucial considerations around reliability, privacy, security, and responsible AI. With its rigorous approach spanning theory and practice across diverse tinyML application domains like smart homes, wearables, and industrial IoT, the book enables readers to develop specialized knowledge. Using concrete use cases and hands-on examples, readers will learn to apply machine learning to transform embedded and IoT systems. Overall, this indispensable guide provides a research-based foundation for leveraging machine learning in embedded systems.
+  subtitle: "with TinyML"
+  abstract: Machine Learning Systems with TinyML offers an introduction to end-to-end machine learning pipelines by focusing on their implementation on resource-constrained devicesds. TinyML provides an accessible lens through which to cover general principles and best practices for applying machine learning. As edge computing grows, performant TinyML enables AI on embedded devices. This book provides the expertise to deploy complete machine learning systems - from data preparation to model training, optimization, acceleration and production deployment - seen through the lens of tinyML applications. The book covers important topics like efficient neural network architectures, hardware-aware training, model compression, benchmarking, and on-device learning. Additional chapters highlight advances like on-device data generation and considerations around reliability, privacy, security, and responsible AI. With concrete use cases and hands-on examples, readers will learn to implement end-to-end machine learning workflows on embedded devices. Overall, by grounding the introduction in tinyML applications, the book enables readers to develop specialized knowledge while also learning general concepts for applying machine learning systems to transform edge devices and IoT.
+
+
   search: true
   repo-url: https://github.com/harvard-edge/cs249r_book
   repo-actions: [edit, issue, source]

index.qmd

@@ -1,8 +1,8 @@
 # Preface {.unnumbered}
 
-Welcome to "Machine Learning Systems for TinyML" This book is your gateway to the fast-paced world of artificial intelligence within embedded systems. It as an extension of the foundational course, tinyML from CS249r at Harvard University.
+Welcome to "Machine Learning Systems for TinyML" This book is your gateway to the fast-paced world of artificial intelligence through the lens of embedded systems. It as an extension of the foundational course, tinyML from [CS249r](https://sites.google.com/g.harvard.edu/cs249-tinyml-2023) at Harvard University.
 
-Our aim? To make this book a collaborative effort that brings together insights from students, professionals, and the broader community. We want to create a one-stop guide that dives deep into the nuts and bolts of embedded AI and its many uses.
+Our aim is to make this open-soruce book a collaborative effort that brings together insights from students, professionals, and the broader community of applied machine learning practitioners. We want to create a one-stop guide that dives deep into the nuts and bolts of AI systems and its many uses.
 
 > "If you want to go fast, go alone. If you want to go far, go together."
 > -- African Proverb