When one asks what the technology trends are, the answer you will hear often is “ABCD.” A stands for artificial intelligence (AI), B for blockchain technology, C for cloud computing, and D for big data. ABCD reveals what the various industries are thinking about and what most companies are looking at. To combine all of these technologies, there is now a lot of stored data to be analyzed using artificial intelligence technology, and to be secured by the blockchain technology; finally, all of these are done in the cloud.

This book, Cloud computing for machine learning and cognitive applications, includes material all pertaining to ABCD, though maybe in different proportions. It is a comprehensive book with over 600 pages. It can be used as a textbook for a two-semester course. For practitioners, it is a good reference book.

The book has four parts. Part 1 is “Cloud, Big Data, and Cognitive Computing.” This part basically explains the concepts behind these terms, including cloud computing, big data, data analytics, the Internet of Things (IoT), and cognitive computing.

Part 2 introduces the cloud architecture and service platform design. First, virtual machines, docker containers, and server clusters are explained. Some cloud virtualization systems are also covered. Next, the most important concepts in cloud computing such as cloud architectures and platform design are explained in detail. The third chapter in this part is very important. It explains the mobile cloud, IoT, and cloud mashup services. To understand and apply recent cloud technologies, this chapter is a must read.

Part 3 includes the principles of machine learning and artificial intelligence machines. The part has two chapters. One covers basic machine learning algorithms and model fitting, and the other moves further to explain the hot topics of deep learning and intelligent machines.

Part 4 is the practical part. It teaches cloud programming and how to improve cloud performance. Hadoop and Spark are introduced in the first chapter. However, to master them you need further reading and real practice. TensorFlow, Keras, DeepMind, and graph analytics are briefly covered in the second chapter of this part. Finally, performance issues, security, and privacy concerns are laid out.

Overall, this book touches on a lot of topics. However, every topic is only discussed superficially, maybe in less detail than what one could learn from Wikipedia. Nevertheless, the concepts of modern cloud computing, big data, and AI are included--a difficult task well done!