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@@ -555,7 +555,8 @@ <h1 class="title">Machine Learning Systems</h1>
     <div class="block-title">Abstract</div>
     <p>Machine Learning Systems 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 focuses on demystifying 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!</p>
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-    <p>🎙 Explore the <strong><a href="https://notebooklm.google.com/notebook/bae2e128-7926-4ba9-8503-2b54ff3237f9/audio">Podcast</a></strong>: This unique podcast was automatically generated by Google’s Notebook LM.</p>
+    <p>🎙 Explore the <strong><a href="notebooklm_podcast_mlsysbookai.mp3">Podcast</a></strong>: This podcast was automatically created by Google’s Notebook LM, based on insights drawn from our <a href="https://web.eng.fiu.edu/gaquan/Papers/ESWEEK24Papers/CPS-Proceedings/pdfs/CODES-ISSS/563900a043/563900a043.pdf">IEEE position paper</a>.</p>
+    <p>&nbsp; <audio controls=""> <source src="notebooklm_podcast_mlsysbookai.mp3" type="audio/mpeg"> </audio></p>
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