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This chapter starts with why to choose ML. It covers various types of supervised and unsupervised machine learning algorithms with python code. Various performance measures are also to measure the performance of various algorithms are explained, and popular ML platforms are covered in the chapter.

This chapter examines the evolution of cloud computing and how the history of computing has swung between remote and local computers. It uses NIST definition to examine five essential features of any cloud computing service. Then the three service models of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are examined. Furthermore, we look into the deployment models of private, community, public, and hybrid clouds. Next, we will study the value-added chain of various cloud stakeholders, including their pain points. Lastly, a case will be made for conducting AI and ML in cloud. It will include a workflow for creating an ML model, with some real-life examples. We wrap up this chapter by looking at the future trends that include targeted hardware.

In this chapter, we will start by examining traditional information security and aspects of confidentiality, integrity, and access control. We will look at ways to protect data at rest, during transit, and in use. Then the security concerns arising out of cloud operating models will be examined. We will present seven recommended security practices to protect data in the cloud. Lastly, role of ML for cybersecurity will be investigated.

In this chapter, we focus on the practical aspects in machine learning. Framework for data preparation techniques is looked into. Various practical aspects for implementation of machine learning are discussed, like preprocessing, dimension reduction techniques, feature engineering, and methods for feature selection. We wrap up the chapter by looking into the techniques for dealing with imbalanced data.

Machine learning is a latest buzzword floating around everywhere. It is one of the most interesting sub-fields of computer science. In this chapter, we will introduce the basic concept of machine learning. We will cover various paradigms of learning and applications of machine learning. Next, we will cover the justification of using machine learning and various tasks where machine learning is used. In the end, we discuss about various tools used.

We start this chapter by revisiting the history of AI and its current limitations. In response, emergence of AI hardware accelerators will be studied, starting with GPUs. Next, we will study the use of FPGAs, ASICs, TPUs, VPUs, and a massive wafer scale AI engine. Several examples and products will be presented wrapping up with an ecosystem example.

This is chapter to examine the practical aspects of running data analytics in a Public Cloud with several real-life examples. We will discuss three major public Cloud Service Providers (CSPs), namely Amazon’s AWS, Google’s GCP, and MS Azure with their key ML-related offerings. A popular framework of MapReduce with an introduction of Hadoop will be examined. Lastly, practical examples of cloud-based AI and ML services will be presented. We will wrap up the chapter with future possibilities of using reinforced learning.

In this chapter, we introduce the concept of deep learning, and various deep learning architectures are covered. Difference between machine learning and deep learning is discussed. Justification for using DL is covered. Popular software and platform for deep learning are explored.