Data-Centric Artificial Intelligence for Multidisciplinary Applications
Data-Centric Artificial Intelligence for Multidisciplinary Applications by Parikshit N Mahalle, published by CRC Press in 2024, is a comprehensive exploration of the data-centric AI approach and its applications across various fields. This 294-page book delves into the methodologies that distinguish data-centric approaches from traditional model-centric methods, emphasizing the importance of edge AI in contrast to cloud-based solutions. It introduces the concept of data-centric AI (DCAI), which prioritizes understanding and utilizing data to enhance decision-making and outcomes.
Readers will find a detailed examination of the challenges associated with dataset generation, including the use of synthetic datasets and prediction algorithms. The book also includes practical case studies, particularly in healthcare and agriculture, showcasing the impact of quality data on AI applications. By integrating machine learning and big data analytics, this edition highlights how data-centric AI can lead to more informed choices and improved scalability compared to conventional AI methods.
Official synopsis Publisher
This book explores the need for a data‑centric AI approach and its application in the multidisciplinary domain, compared to a model‑centric approach. It examines the methodologies for data‑centric approaches, the use of data‑centric approaches in different domains, the need for edge AI and how it differs from cloud‑based AI. It discusses the new category of AI technology, “data‑centric AI” (DCAI), which focuses on comprehending, utilizing, and reaching conclusions from data. By adding machine learning and big data analytics tools, data‑centric AI modifies this by enabling it to learn from data rather than depending on algorithms. It can therefore make wiser choices and deliver more precise outcomes. Additionally, it has the potential to be significantly more scalable than conventional AI methods.
* Includes a collection of case studies with experimentation results to adhere to the practical approaches
* Examines challenges in dataset generation, synthetic datasets, analysis, and prediction algorithms in stochastic ways
* Discusses methodologies to achieve accurate results by improving the quality of data
* Comprises cases in healthcare and agriculture with implementation and impact of quality data in building AI applications
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