With the emergence of revolutionary technological standards such as 5G and Industry 4.0, real time applications which require both cloud computing and machine learning are becoming increasingly common. Examples of such applications include real-time scheduling and resource allocation in cloud radio access networks, real-time process monitoring and control in industrial Internet of Things, network traffic analysis, short-term weather forecasting, and robotics. Given the increase in such applications, several cloud service providers such as Microsoft Azure Machine Learning, IBM Watson, and Google AI have started incorporating Artificial Intelligence (AI) applications on their platforms as well as providing Analytics as a Service. While it is now simple for users to deploy AI or machine learning algorithms using these cloud platforms, researchers from academia and industry can also develop their own machine learning applications and run them on these platforms to benefit from high processing power and global deploy ability. The main purpose of this book is to provide in-depth coverage of the programming methodologies and configurations required in developing real-time applications that require machine learning algorithms to be hosted on cloud computing platforms to leverage storage and computing resources.
With detailed explanations on all fundamental concepts, programming techniques, and configuration steps in developing cloud hosted machine learning applications, this book will provide excellent guidance and a full hands-on experience to researchers, professionals and students working in this field.
Table of Contents
Chapter 1. Introduction
Chapter 2. Cloud Computing Fundamentals
Chapter 3. Machine Learning Algorithms
Chapter 4. Data Capture and Client Architecture for a Cloud-Based Real-Time Network Analytics System
Chapter 5. Server and Servlet Architectures for a Cloud-Based Real-Time Network Analytics System
Chapter 6. Data Capture and Client Architecture for a Cloud-Based Real-Time Weather Forecasting System
Chapter 7. Server and Servlet Architectures for a Cloud-Based Real-Time Weather Forecasting System
“The increasing adoption of cloud computing and machine learning for real-time applications makes this textbook very timely and valuable. Its contents are presented in an accessible form, enhanced by many clear and well-illustrated examples. Opening with a comprehensive coverage of the fundamentals of both topics, it goes on to explain how to integrate them into a real-time network analytics system. Real-time examples enable the reader to gain a clear understanding of the technology and its applications. It is highly recommended for practitioners and researchers alike.” – Peter J. Fleming, Emeritus Professor, Department of Automatic Control and Systems Engineering, The University of Sheffield, UK