This book vividly illustrates all the promising and potential machine learning (ML) and deep learning (DL) algorithms through a host of real-world and real-time business use cases. Machines and devices can be empowered to self-learn and exhibit intelligent behavior. Also, Big Data combined with real-time and runtime data can lead to personalized, prognostic, predictive, and prescriptive insights. This book examines the following topics: Cognitive machines and devices Cyber physical systems (CPS) The Internet of Things (IoT) and industrial use cases Industry 4.0 for smarter manufacturing Predictive and prescriptive insights for smarter systems Machine vision and intelligence Natural interfaces K-means clustering algorithm Support vector machine (SVM) algorithm A priori algorithms Linear and logistic regression Applied Learning Algorithms for Intelligent IoT clearly articulates ML and DL algorithms that can be used to unearth predictive and prescriptive insights out of Big Data. Transforming raw data into information and relevant knowledge is gaining prominence with the availability of data processing and mining, analytics algorithms, platforms, frameworks, and other accelerators discussed in the book. Now, with the emergence of machine learning algorithms, the field of data analytics is bound to reach new heights. This book will serve as a comprehensive guide for AI researchers, faculty members, and IT professionals. Every chapter will discuss one ML algorithm, its origin, challenges, and benefits, as well as a sample industry use case for explaining the algorithm in detail. The book's detailed and deeper dive into ML and DL algorithms using a practical use case can foster innovative research.

This book constitutes the refereed and selected papers presented during the Third International Conference on Internet of Things, ICIoT 2022, held in Chennai, India, in April 2022.  The 10 papers were thoroughly reviewed and selected from the 100 qualified submissions. They focus on application of IoT in the field of Computer Science and Information Technology as well as on Industrial IoT.

Numerous advancements are being brought in and incorporated into the cloud domain with the aim of realizing a trove of deeper and decisive automations. Rather than discussing the cloud paradigm in isolation, this fully updated text examines how cloud computing can work collaboratively with other computing models to meet the needs of evolving trends.This multi-dimensional approach encompasses the challenges of fulfilling the storage requirements of big data, the use of the cloud as a remote server for Internet of Things and sensor networks, and an investigation of how cloud computing is interlinked with other established computing phenomenon such as edge computing. New chapters illustrate the distinct ideals of the cloud-native computing, proclaimed as the next-generation cloud computing paradigm. Topics and features: Includes learning objectives, motivating questions, and self-test exercises Introduces the underlying concepts, fundamental features, and key technological foundations of cloud computing Examines how enterprise networking and cloud networking can work together to achieve business goals Reviews the different types of cloud storage available to address the evolution of data and the need for digitization Discusses the challenges and approaches to implementing cloud governance, security, and the hot topic of cloud management Describes the details of cloud migration, the crucial role of monitoring in optimizing the cloud, and the basics of disaster recovery using cloud infrastructure This technically rigorous, yet simple-to-follow textbook is an ideal resource for graduate courses on cloud computing. Professional software developers and cloud architects will also find the work to be an invaluable reference.

Numerous advancements are being brought in and incorporated into the cloud domain with the aim of realizing a trove of deeper and decisive automations. Rather than discussing the cloud paradigm in isolation, this fully updated text examines how cloud computing can work collaboratively with other computing models to meet the needs of evolving trends.This multi-dimensional approach encompasses the challenges of fulfilling the storage requirements of big data, the use of the cloud as a remote server for Internet of Things and sensor networks, and an investigation of how cloud computing is interlinked with other established computing phenomenon such as edge computing. New chapters illustrate the distinct ideals of the cloud-native computing, proclaimed as the next-generation cloud computing paradigm. Topics and features: Includes learning objectives, motivating questions, and self-test exercises Introduces the underlying concepts, fundamental features, and key technological foundations of cloud computing Examines how enterprise networking and cloud networking can work together to achieve business goals Reviews the different types of cloud storage available to address the evolution of data and the need for digitization Discusses the challenges and approaches to implementing cloud governance, security, and the hot topic of cloud management Describes the details of cloud migration, the crucial role of monitoring in optimizing the cloud, and the basics of disaster recovery using cloud infrastructure This technically rigorous, yet simple-to-follow textbook is an ideal resource for graduate courses on cloud computing. Professional software developers and cloud architects will also find the work to be an invaluable reference.

Create, deploy, and manage applications at scale using SRE principles Key Features Build and run highly available, scalable, and secure software Explore abstract SRE in a simplified and streamlined way Enhance the reliability of cloud environments through SRE enhancements Book DescriptionSite reliability engineering (SRE) is being touted as the most competent paradigm in establishing and ensuring next-generation high-quality software solutions. This book starts by introducing you to the SRE paradigm and covers the need for highly reliable IT platforms and infrastructures. As you make your way through the next set of chapters, you will learn to develop microservices using Spring Boot and make use of RESTful frameworks. You will also learn about GitHub for deployment, containerization, and Docker containers. Practical Site Reliability Engineering teaches you to set up and sustain containerized cloud environments, and also covers architectural and design patterns and reliability implementation techniques such as reactive programming, and languages such as Ballerina and Rust. In the concluding chapters, you will get well-versed with service mesh solutions such as Istio and Linkerd, and understand service resilience test practices, API gateways, and edge/fog computing. By the end of this book, you will have gained experience on working with SRE concepts and be able to deliver highly reliable apps and services. What you will learn Understand how to achieve your SRE goals Grasp Docker-enabled containerization concepts Leverage enterprise DevOps capabilities and Microservices architecture (MSA) Get to grips with the service mesh concept and frameworks such as Istio and Linkerd Discover best practices for performance and resiliency Follow software reliability prediction approaches and enable patterns Understand Kubernetes for container and cloud orchestration Explore the end-to-end software engineering process for the containerized world Who this book is forPractical Site Reliability Engineering helps software developers, IT professionals, DevOps engineers, performance specialists, and system engineers understand how the emerging domain of SRE comes handy in automating and accelerating the process of designing, developing, debugging, and deploying highly reliable applications and services.