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AI Engineering
Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach. AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI. AI Engineering builds upon and is complementary to Designing Machine Learning Systems (O'Reilly).
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Recent Advances in Artificial Intelligence and Data Engineering
This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering.
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Engineering Artificially Intelligent Systems
Many current AI and machine learning algorithms and data and information fusion processes attempt in software to estimate situations in our complex world of nested feedback loops. Such algorithms and processes must gracefully and efficiently adapt to technical challenges such as data quality induced by these loops, and interdependencies that vary in complexity, space, and time.To realize effective and efficient designs of computational systems, a Systems Engineering perspective may provide a framework for identifying the interrelationships and patterns of change between components rather than static snapshots. We must study cascading interdependencies through this perspective to understand their behavior and to successfully adopt complex system-of-systems in society. This book derives in part from the presentations given at the AAAI 2021 Spring Symposium session on Leveraging Systems Engineering to Realize Synergistic AI / Machine Learning Capabilities. Its 16 chapters offer an emphasis on pragmatic aspects and address topics in systems engineering; AI, machine learning, and reasoning; data and information fusion; intelligent systems; autonomous systems; interdependence and teamwork; human-computer interaction; trust; and resilience.
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Handbook of AI in Engineering Applications
There is a need to categorize artificial intelligence (AI) applications, tools, techniques, and algorithms based on their intended use in various design stages. Specifically, there is a need to explore AI techniques that are utilized for tasks such as designing, including but not limited to inspiration, idea and concept generation, concept evaluation, optimization, decision-making, and modeling. This includes things like generating ideas and concepts, evaluating those ideas, optimizing designs, making decisions, and creating models. This handbook brings all of these categories with compatible AI techniques, tools, and algorithms together in one place.Handbook of AI in Engineering Applications: Tools, Techniques, and Algorithms covers applications of AI in engineering and highlights areas such as future cities, mechanical system analysis, and robotic process automation, and presents the application of AI and the use of computerized systems that aim to simplify and automate the processes of design and construction of civil works. The handbook discusses the design and optimization of mechanical systems and parts, such as engines, gears, and bearings, which can be automated using AI and it explores the performance of robotics and automation systems which can be simulated and analyzed using AI to forecast behavior, spot future issues, and suggest changes. Rounding out this handbook is AI technology automation and how analyzing relevant data can provide a reliable basis for relevant personnel to carry out their work.This handbook fills the gap between R&D in AI and will benefit all stakeholders including industries, professionals, technologists, academics, research scholars, senior graduate students, government, and public healthcare professionals.
R 3,297.00
Artificial Intelligence and Online Engineering
Nowadays, online technologies are the core of most fields of engineering and the whole society and are inseparable connected for example withInternet of Things & Industrial Internet of Things (Industry 4.0),Online & Biomedical Engineering,Data Science, Machine Learning, and Artificial Intelligence,Cross & Mixed Reality, andRemote Working Environments. to name only a few.Since the first REV conference in 2004, we tried to focus on the upcoming use of the Internet for engineering tasks and the opportunities as well as challenges around it. Consequently, the motto of this year’s REV2022 was “Artificial Intelligence and Online Engineering”.In a globally connected world, the interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. In response to that, the general objective of this conference is to contribute and discuss fundamentals, applications, and experiences in the field of Online and Remote Engineering, Virtual Instrumentation and other related new technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber-Security, and M2M & Smart Objects. Another objective of the conference is to discuss guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and Open Resources.REV2022 was the 19th in a series of annual events concerning the area of Online Engineering. It has been organized in cooperation with The British University in Egypt (BUE), Cairo, as a hybrid event from February 28 until March 02, 2022.
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Introduction to Artificial Intelligence in Automotive Engineering
This book serves as a practical and timely guide to understanding how artificial intelligence can be applied and leveraged in automotive engineering. It demystifies complex methods of applying artificial intelligence and provides readers with the tools to creatively and immediately apply them to their own engineering challenges, transforming information into knowledge.Drawing on the authors' teaching experience, the book takes a practical approach to applying artificial intelligence tools - such as regression, classification, clustering, deep learning and reinforcement learning- to real-world engineering problems. Through clear explanations and step-by-step algorithmic structures, readers are introduced to the fundamental concepts of artificial intelligence and their practical application by building AI models using automotive data. Examples include predicting the energy consumption of electric vehicles under varying environmental and operating conditions, assessing battery degradation, analyzing brake system wear, and implementing intelligent control for thermal management (HVAC systems). The book shows how the application of artificial intelligence techniques and methods can optimize performance, increase safety, and stimulate innovation in the automotive sector.The book represents an essential guide for all those interested in exploring the application of artificial intelligence in automotive engineering (and beyond) and equally constitutes an effective support in the teaching process (courses, laboratories, projects) of applied artificial intelligence concepts.
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Introduction to Artificial Intelligence in Automotive Engineering
This book serves as a practical and timely guide to understanding how artificial intelligence can be applied and leveraged in automotive engineering. It demystifies complex methods of applying artificial intelligence and provides readers with the tools to creatively and immediately apply them to their own engineering challenges, transforming information into knowledge.Drawing on the authors' teaching experience, the book takes a practical approach to applying artificial intelligence tools - such as regression, classification, clustering, deep learning and reinforcement learning- to real-world engineering problems. Through clear explanations and step-by-step algorithmic structures, readers are introduced to the fundamental concepts of artificial intelligence and their practical application by building AI models using automotive data. Examples include predicting the energy consumption of electric vehicles under varying environmental and operating conditions, assessing battery degradation, analyzing brake system wear, and implementing intelligent control for thermal management (HVAC systems). The book shows how the application of artificial intelligence techniques and methods can optimize performance, increase safety, and stimulate innovation in the automotive sector.The book represents an essential guide for all those interested in exploring the application of artificial intelligence in automotive engineering (and beyond) and equally constitutes an effective support in the teaching process (courses, laboratories, projects) of applied artificial intelligence concepts.
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Handbook of AI in Engineering Applications
There is a need to categorize artificial intelligence (AI) applications, tools, techniques, and algorithms based on their intended use in various design stages. Specifically, there is a need to explore AI techniques that are utilized for tasks such as designing, including but not limited to inspiration, idea and concept generation, concept evaluation, optimization, decision-making, and modeling. This includes things like generating ideas and concepts, evaluating those ideas, optimizing designs, making decisions, and creating models. This handbook brings all of these categories with compatible AI techniques, tools, and algorithms together in one place.Handbook of AI in Engineering Applications: Tools, Techniques, and Algorithms covers applications of AI in engineering and highlights areas such as future cities, mechanical system analysis, and robotic process automation, and presents the application of AI and the use of computerized systems that aim to simplify and automate the processes of design and construction of civil works. The handbook discusses the design and optimization of mechanical systems and parts, such as engines, gears, and bearings, which can be automated using AI and it explores the performance of robotics and automation systems which can be simulated and analyzed using AI to forecast behavior, spot future issues, and suggest changes. Rounding out this handbook is AI technology automation and how analyzing relevant data can provide a reliable basis for relevant personnel to carry out their work.This handbook fills the gap between R&D in AI and will benefit all stakeholders including industries, professionals, technologists, academics, research scholars, senior graduate students, government, and public healthcare professionals.
R 9,954.94
Artificial Intelligence and Machine Learning Algorithms for Engineering Applications
This book comprehensively covers core algorithms and techniques used in artificial intelligence (AI) and machine learning (ML) for engineering applications. It further explores the use of AI in civil and structural engineering, quality control, and product design.Features:Presents autonomous robots using onboard computing and artificial intelligence (AI) algorithms to process the data from their sensors and make real-time decisionsDiscusses nature-based optimization-based computing techniques to enhance the computational speed for solving engineering problemsProvides conceptual and practical knowledge about the design of modern computation techniques with advanced tools and methodologiesHighlights the importance of using smart techniques including AI and ML in product design and developmentCovers time series analysis and forecasting in engineering, robotic process automation, and autonomous robots in manufacturingThe text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, manufacturing engineering, and environmental engineering.
R 8,248.58
Artificial Intelligence, Engineering Systems and Sustainable Development
Development in any country is impossible if reliable and affordable energy, safe water and sanitation, as well as telecommunication facilities, are not easily accessible. Artificial intelligence and machine learning techniques are now widely used in all branches of engineering to build and optimize systems. The combination of AI and engineering can indeed act as a real catalyst to achieve the UN SDGs.The volume editors present an analysis of different concepts and case studies in engineering disciplines such as chemical, civil, electrical, telecommunications and mechanical engineering, demonstrating how engineering systems and processes can leverage the power of AI to drive and achieve the UN SDGs. Topics covered include sustainable crop production and consumption, AI based clean water and sanitation monitoring, intelligent transport systems and achieving affordable and clean energy through AI and 5G powered internet of energy.Such a study is of paramount importance and is a valuable source of information for researchers, engineers, and policy makers to be able to better design and adopt AI enabled techniques in different engineering areas, with a view to catalyze the achievement of the UN SDGs.
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R 6,423.16
Advances in Artificial Intelligence, Software and Systems Engineering
This book focuses on emerging issues following the integration of artificial intelligence systems in our daily lives. It focuses on the cognitive, visual, social and analytical aspects of computing and intelligent technologies, highlighting ways to improve technology acceptance, effectiveness, and efficiency. Topics such as responsibility, integration and training are discussed throughout. The book also reports on the latest advances in systems engineering, with a focus on societal challenges and next-generation systems and applications for meeting them. It also discusses applications in smart grids and infrastructures, systems engineering education as well as defense and aerospace. The book is based on both the AHFE 2018 International Conference on Human Factors in Artificial Intelligence and Social Computing, Software and Systems Engineering, The Human Side of Service Engineering and Human Factors in Energy, July 21–25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA.
R 8,756.45
New Paradigm in AI and Concurrent Engineering
This book includes research papers presented at the International Conference on AI and Concurrent Engineering (AICE 2025) to be held at IIMT, Bhubaneswar, India, during August 23 – 24, 2025. The proceedings documents how AI systems analyse vast datasets, predict outcomes, and offer strategic insights by leveraging both historical and real-time information. The proceedings also explores how AI-driven predictive maintenance and quality control are transforming post-production processes.
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Artificial Intelligence and System Engineering
This book presents cutting-edge research from CoMeSySo 2024, bridging theoretical insights and practical solutions across modern computing. Highlighting data-driven methodologies, innovative modelling, and robust optimization strategies, this book uniquely integrates interdisciplinary approaches to complex computational problems. Through real-world case studies, readers gain actionable insights applicable to diverse fields such as artificial intelligence, cybersecurity, e-learning, and operational logistics. Targeted at academic researchers, industry specialists, and graduate students, the compilation provides valuable frameworks and advanced analytical tools for enhancing performance, efficiency, and decision-making. The presented studies, encompassing adaptive network modelling, deep learning applications, and novel AI-driven frameworks, equip readers to tackle emerging challenges and drive future advancements effectively.
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Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering
In today's developing world, industries are constantly required to improve and advance. New approaches are being implemented to determine optimum values and solutions for models such as artificial intelligence and machine learning. Research is a necessity for determining how these recent methods are being applied within the engineering field and what effective solutions they are providing.Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering is a collection of innovative research on the methods and implementation of machine learning and AI in multiple facets of engineering. While highlighting topics including control devices, geotechnology, and artificial neural networks, this book is ideally designed for engineers, academicians, researchers, practitioners, and students seeking current research on solving engineering problems using smart technology.
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Artificial Intelligence for Engineers
This textbook presents basic knowledge and essential toolsets needed for people who want to step into artificial intelligence (AI). The book is especially suitable for those college students, graduate students, instructors, and IT hobbyists who have an engineering mindset. That is, it serves the idea of getting the job done quickly and neatly with an adequate understanding of why and how. It is designed to allow one to obtain a big picture for both AI and essential AI topics within the shortest amount of time.
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Systems Engineering and Artificial Intelligence
This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.
R 6,601.26
Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence
Artificial intelligence (AI) is influencing the future of almost every sector and human being. AI has been the primary driving force behind emerging technologies such as big data, blockchain, robots, and the internet of things (IoT), and it will continue to be a technological innovator for the foreseeable future. New algorithms in AI are changing business processes and deploying AI-based applications in various sectors. The Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence is a comprehensive reference that presents cases and best practices of AI and knowledge engineering applications on business intelligence. Covering topics such as deep learning methods, face recognition, and sentiment analysis, this major reference work is a dynamic resource for business leaders and executives, IT managers, AI scientists, students and educators of higher education, librarians, researchers, and academicians.
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Systems Engineering and Artificial Intelligence
This book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.
R 5,936.95
Artificial Intelligence and Machine Learning Techniques in Engineering and Management
The present book covers various facets of Artificial Intelligence, Machine Learning, and Fuzzy Logic. It includes a brief discussion on performance indicators, Classical and Advanced Machine Learning algorithms, Fuzzy logic-based modelling algorithms, Emerging Research Areas, including Blockchain, recent ML techniques, Evolutionary Algorithms, Large Language Model (LLM)-based Generative AI, the Internet of Things, Big Data, Decision Support Systems, Taguchi design of experiments, data augmentation, and Cross-Validation, and representative case studies. The appendix covers representative AI tools, data sources, books, and journals on AI. The present book can support undergraduate, postgraduate, and Ph.D. students in Artificial Intelligence, Generative Artificial Intelligence, Machine Learning, Data Sciences, Soft Computing, and Fuzzy Logic in Engineering and Management and allied fields. The proposed book has immense value in the interdisciplinary and cross-disciplinary context.
R 3,663.00
Handbook of Research on AI Methods and Applications in Computer Engineering
The development of artificial intelligence (AI) involves the creation of computer systems that can do activities that would ordinarily require human intelligence, such as visual perception, speech recognition, decision making, and language translation. Through increasingly complex programming approaches, it has been transforming and advancing the discipline of computer science. Artificial Intelligence Methods and Applications in Computer Engineering illuminates how today's computer engineers and scientists can use AI in real-world applications. It focuses on a few current and emergent AI applications, allowing a more in-depth discussion of each topic. Covering topics such as biomedical research applications, navigation systems, and search engines, this premier reference source is an excellent resource for computer scientists, computer engineers, IT managers, students and educators of higher education, librarians, researchers, and academicians.
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