diff --git a/_quarto.yml b/_quarto.yml
index 27acb3bd1..d96e91496 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -46,8 +46,8 @@ book:
page-navigation: true
title: "Machine Learning Systems"
- subtitle: "with TinyML"
- 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!"
+ subtitle: "with tinyML"
+ 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 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!"
repo-url: https://github.com/harvard-edge/cs249r_book
repo-branch: dev
diff --git a/github-button.html b/github-button.html
index 6b8871d0e..659eb1f76 100644
--- a/github-button.html
+++ b/github-button.html
@@ -4,40 +4,55 @@
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