Building Cloud Software Products: Innovation, Technology, and Product ManagementКНИГИ » СЕТЕВЫЕ ТЕХНОЛОГИИ
Название: Building Cloud Software Products: Innovation, Technology, and Product Management Автор: Yasin Hajizadeh, Alexander Poth, Andreas Riel Издательство: Springer Год: 2025 Страниц: 222 Язык: английский Формат: pdf (true), epub Размер: 19.9 MB
Cloud-native approaches have become essential in IT and OT product development. Cloud-native is more than using the newest cutting-edge services from hyperscalers. Building cloud products benefits from a holistic approach beyond focusing on an isolated cloud paradigm. This book addresses the different aspects of designing, building, and running cloud software products and services from a holistic perspective. It investigates how to empower cloud product and service teams to consider the relevant aspects for long-term success. It provides an overview of selected technologies and practical adoptions and explores various requirements to maintain economic and environmental sustainability. It examines the challenges faced by product management teams of cloud providers, independent software vendors (ISVs), and system integrators (SIs) and offers potential solutions. The chapters also showcase internal success stories and case studies of various companies during the lifecycle of a cloud product.
Offering a combination of advanced research from academia and practical industry lessons learned, this book empowers cloud product and service teams to consider and adopt various ideas, concepts, and methods to provide successful, high-quality cloud products and services.
Cloud-native approaches become essential in IT and OT product development. Cloud-native is more than using the newest cutting-edge services from hyperscalers. Building cloud products benefits from a holistic approach beyond focusing on an isolated cloud paradigm. The book concentrates on a holistic view to empower cloud product and service teams to consider the relevant aspects for their long-term success.
Topics and Features:
• build a specific product and service vision and refine it to a roadmap • establish a life-cycle view: focus the right aspects per life-cycle phase • elaborate a sustainable set of requirements from UX to energy footprint • overview of selected key technologies and practical adoption approaches
Machine Learning (ML) techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data such as supervised learning, unsupervised learning and reinforcement learning. While focusing on the benefit of ML towards business processes, we will focus on supervised learning in this article as it offers not only a wide range of possible applications serving as a perfect link of ML towards business value analysis but also it promises more reliable outputs due to the successive interaction of individual applications within an integrated system and the identification and verification of the right labels. Supervised learning starts with an established set of data and a discrete (classification) or continuous (regression) data, which are important for data analysis of ML. The assignment to the classes is done with a classifier, which represents the model and predicts the classes for input. The target is to find patterns in data that can be applied to an analytics process. The most important supervised learning methods are the fisher discriminant analysis, partial least squares, nearest neighbours, principal component regression, artificial neural networks, support vector machine, Gaussian process regression, decision tree, random forest and so on.
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Building Cloud Software Products: Innovation, Technology, and Product Management 
