Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Joy, Helen K.; Jayarani, Electa Alice; Sridevi, R. Title A name given to the resource TinyML Empowering Intelligent Edge Devices Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AI-Based Solutions for Engineering;pp.71-101 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-0746-6.ch003" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-0746-6.ch003</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017546582?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017546582?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joy H.K., Christ University, India; Jayarani E.A., Kammavari Sangha's Institute of Technology, India; Sridevi R., Christ University, India Description An account of the resource Tiny Machine Learning (TinyML) bridges the gap between artificial intelligence and low-power embedded systems, enabling devices like microcontrollers to process data locally and operate autonomously. This chapter explores the foundational principles of TinyML, its applications across industries such as IoT, healthcare, industrial automation, and environmental monitoring, and the tools enabling its deployment. It also addresses challenges, including energy efficiency and model optimization, while providing insights into future advancements such as federated learning and neuromorphic computing. This chapter offers a comprehensive understanding of TinyML's transformative potential and its pivotal role in AI-based engineering solutions. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833730748-0; 979-833730746-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online