Reliable Machine Learning
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Reliable Machine Learning
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Author : Cathy Chen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-10-12
Reliable Machine Learning written by Cathy Chen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-12 with Computers categories.
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization. By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind. You'll examine: What ML is: how it functions and what it relies on Conceptual frameworks for understanding how ML "loops" work How effective productionization can make your ML systems easily monitorable, deployable, and operable Why ML systems make production troubleshooting more difficult, and how to compensate accordingly How ML, product, and production teams can communicate effectively
Conformal Prediction For Reliable Machine Learning
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Author : Vineeth Balasubramanian
language : en
Publisher: Newnes
Release Date : 2014-04-23
Conformal Prediction For Reliable Machine Learning written by Vineeth Balasubramanian and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-23 with Computers categories.
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Artificial Intelligence For Safety And Reliability Engineering
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Author : Kim Phuc Tran
language : en
Publisher: Springer Nature
Release Date : 2024-09-28
Artificial Intelligence For Safety And Reliability Engineering written by Kim Phuc Tran and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-28 with Technology & Engineering categories.
This book is a comprehensive exploration of the latest theoretical research, technological advancements, and real-world applications of artificial intelligence (AI) for safety and reliability engineering. Smart manufacturing relies on predictive maintenance (PdM) to ensure sustainable production systems, and the integration of AI has become increasingly prevalent in this field. This book serves as a valuable resource for researchers, practitioners, and decision-makers in manufacturing. By combining theoretical research, practical applications, and case studies, it equips readers with the necessary knowledge and tools to implement AI for safety and reliability engineering effectively in smart manufacturing contexts.
Computer Safety Reliability And Security
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Author : Barbara Gallina
language : en
Publisher: Springer
Release Date : 2018-09-03
Computer Safety Reliability And Security written by Barbara Gallina and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-03 with Computers categories.
This book constitutes the refereed proceedings of five workshops co-located with SAFECOMP 2018, the 37th International Conference on Computer Safety, Reliability, and Security, held in Västerås, Sweden, in September 2018. The 28 revised full papers and 21 short papers presented together with 5 introductory papers to each workshop were carefully reviewed and selected from 73 submissions. This year's workshops are: ASSURE 2018 – Assurance Cases for Software-Intensive Systems; DECSoS 2018 – ERCIM/EWICS/ARTEMIS Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems; SASSUR 2018 – Next Generation of System Assurance Approaches for Safety-Critical Systems; STRIVE 2018 – Safety, securiTy, and pRivacy In automotiVe systEms; and WAISE 2018 – Artificial Intelligence Safety Engineering. The chapter '“Boxing Clever”: Practical Techniques for Gaining Insights into Training Data and Monitoring Distribution Shift' is available open access under an Open GovernmentLicense via link.springer.com.
Reliability And Statistics In Transportation And Communication Human Sustainability And Resilience In The Digital Age
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Author : Igor Kabashkin
language : en
Publisher: Springer Nature
Release Date : 2025-03-29
Reliability And Statistics In Transportation And Communication Human Sustainability And Resilience In The Digital Age written by Igor Kabashkin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-29 with Computers categories.
This book reports on cutting-edge theories and methods for analyzing complex systems, such as transportation and communication networks and discusses multi-disciplinary approaches to dependability problems encountered when dealing with complex systems in practice. It presents the most relevant findings discussed at the 24th International Multidisciplinary Conference on Reliability and Statistics in Transportation and Communication (RelStat 2024), which took place as a hybrid event on September 25-28, 2024, in/from Riga, Latvia. The chapters span a broad spectrum of advanced theories and methods, with a special emphasis on smart technologies and algorithms for enhancing sustainability and resilience of transport systems in various sectors.
Meta Heuristic Techniques In Software Engineering And Its Applications
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Author : Mihir Narayan Mohanty
language : en
Publisher: Springer Nature
Release Date : 2022-10-17
Meta Heuristic Techniques In Software Engineering And Its Applications written by Mihir Narayan Mohanty and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-17 with Technology & Engineering categories.
This book discusses an integration of machine learning with metaheuristic techniques that provide more robust and efficient ways to address traditional optimization problems. Modern metaheuristic techniques, along with their main characteristics and recent applications in artificial intelligence, software engineering, data mining, planning and scheduling, logistics and supply chains, are discussed in this book and help global leaders in fast decision making by providing quality solutions to important problems in business, engineering, economics and science. Novel ways are also discovered to attack unsolved problems in software testing and machine learning. The discussion on foundations of optimization and algorithms leads beginners to apply current approaches to optimization problems. The discussed metaheuristic algorithms include genetic algorithms, simulated annealing, ant algorithms, bee algorithms and particle swarm optimization. New developments on metaheuristics attract researchers and practitioners to apply hybrid metaheuristics in real scenarios.
Conformal Prediction For Enhanced Reliability In Medical Diagnosis Ai
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Author : Etienne Noumen
language : en
Publisher: Etienne Noumen
Release Date :
Conformal Prediction For Enhanced Reliability In Medical Diagnosis Ai written by Etienne Noumen and has been published by Etienne Noumen this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
The book discusses Conformal Prediction (CP) as a method for enhancing the reliability of AI in medical diagnosis by providing rigorous uncertainty quantification. It explains that unlike traditional AI which gives single predictions, CP produces a set of possible outcomes with a guaranteed probability of containing the true answer, addressing the critical need for trustworthy AI in healthcare. The text explores the foundational concepts of CP, compares it to other uncertainty quantification techniques, highlights advanced CP methods for more nuanced guarantees, and surveys its diverse applications in medical imaging, genomics, clinical risk prediction, and drug discovery. Finally, it examines the challenges of clinical integration, the need for human-AI interaction, and the ethical and regulatory dimensions, positioning CP as a vital tool for the safe and effective deployment of AI in medicine despite requiring further research and adaptation for practical success.
Advanced Concretes And Their Structural Applications Volume Ii
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Author : Zhigang Zhang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-07-10
Advanced Concretes And Their Structural Applications Volume Ii written by Zhigang Zhang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-10 with Science categories.
An Examination Of Emerging Bioethical Issues In Biomedical Research
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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2020-09-10
An Examination Of Emerging Bioethical Issues In Biomedical Research written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-10 with Medical categories.
On February 26, 2020, the Board on Health Sciences Policy of the National Academies of Sciences, Engineering, and Medicine hosted a 1-day public workshop in Washington, DC, to examine current and emerging bioethical issues that might arise in the context of biomedical research and to consider research topics in bioethics that could benefit from further attention. The scope of bioethical issues in research is broad, but this workshop focused on issues related to the development and use of digital technologies, artificial intelligence, and machine learning in research and clinical practice; issues emerging as nontraditional approaches to health research become more widespread; the role of bioethics in addressing racial and structural inequalities in health; and enhancing the capacity and diversity of the bioethics workforce. This publication summarizes the presentations and discussions from the workshop.
Towards Reliable Machine Learning In Evolving Data Streams
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Author : Johannes Haug
language : en
Publisher:
Release Date : 2022
Towards Reliable Machine Learning In Evolving Data Streams written by Johannes Haug and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Data streams are ubiquitous in many areas of modern life. For example, applications in healthcare, education, finance, or advertising often deal with large-scale and evolving data streams. Compared to stationary applications, data streams pose considerable additional challenges for automated decision making and machine learning. Indeed, online machine learning methods must cope with limited memory capacities, real-time requirements, and drifts in the data generating process. At the same time, online learning methods should provide a high predictive quality, stability in the presence of input noise, and good interpretability in order to be reliably used in practice. In this thesis, we address some of the most important aspects of machine learning in evolving data streams. Specifically, we identify four open issues related to online feature selection, concept drift detection, online classification, local explainability, and the evaluation of online learning methods. In these contexts, we present new theoretical and empirical findings as well as novel frameworks and implementations. In particular, we propose new approaches for online feature selection and concept drift detection that can account for model uncertainties and thus achieve more stable results. Moreover, we introduce a new incremental decision tree that retains valuable interpretability properties and a new change detection framework that allows for more efficient explanations based on local feature attributions. In fact, this is one of the first works to address intrinsic model interpretability and local explainability in the presence of incremental updates and concept drift. Along with this thesis, we provide extensive open resources related to online machine learning. Notably, we introduce a new Python framework that enables simplified and standardized evaluations and can thus serve as a basis for more comparable online learning experiments in the future. In total, this thesis is based on six publications, five of which were peer-reviewed at the time of publication of this thesis. Our work touches all major areas of predictive modeling in data streams and proposes novel solutions for efficient, stable, interpretable and thus reliable online machine learning.