Advanced Applications in Osmotic Computing
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The interaction of various service models, including edge computing and cloud computing, are quickly changing to better support microservices. This intricate weave of technology and information sharing is necessary to build systems that run faster and more efficiently. The interplay between these computing methods and microservices is emerging as the field of Osmotic Computing. Experts can now embark on an intellectual journey into data-driven exploration and ingenuity with the guidance of the book, Advanced Applications in Osmotic Computing . As ethical considerations become rising concerns, ...
The interaction of various service models, including edge computing and cloud computing, are quickly changing to better support microservices. This intricate weave of technology and information sharing is necessary to build systems that run faster and more efficiently. The interplay between these computing methods and microservices is emerging as the field of Osmotic Computing. Experts can now embark on an intellectual journey into data-driven exploration and ingenuity with the guidance of the book, Advanced Applications in Osmotic Computing . As ethical considerations become rising concerns, the potential biases, privacy encumbrances, and equitable conundrums of osmotic computing are investigated. This book offers judicious strategies to navigate these quandaries conscientiously, adding a layer of responsibility to the discourse. Within these pages, the very fabric of understanding in IoT, Cloud, Edge, Fog, and Machine Learning is redefined, marking a pivotal shift in the paradigm of technological comprehension. This book is an epicenter for the latest evolutions in osmotic computing, unfurling unconventional methodologies that shape the trajectory of data-driven decision-making. Readers will plunge into the theoretical bedrock, simultaneously witnessing pragmatic applications that adeptly bridge the schism between the theoretical constructs and pragmatic realization. The intended audience is multifaceted, encompassing data scientists, machine learning engineers, researchers, academics, educators, students, industry practitioners, interdisciplinary experts, and technology and business leaders.
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