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The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language‘s machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery.
The book contains 10 chapters, the first chapter highlights ML quests, chapter 2 provides a detailed historical perspective, chapter 3 shows how ML works by introducing conceptual frameworks of ML, chapter 4 lists some of the metrics used to assess the performance of ML types. Chapter 5, 6 and 7 focus on different types of ML including supervised, unsupervised and reinforced learning. Chapter 8 and chapter 9 introduce the ML implementation platforms of R and Spark with their different libraries including Spark MLlib. Chapter 10 provides different walk-through ML examples using both R and Spark ML techniques.
Big Data has shown its tremendous potential to transform industries, such as healthcare and insurance industries, and to empower artificial intelligence and machine learning at an unequivocal scale. However, there are concerns that Big Data may lose much of its usefulness, potentially generating new unintended consequences if epistemological (explanatory frameworks of knowledge generated) challenges are not addressed. Big Data has grown tremendously rapidly leading to data to outpace concepts. Conceptual investigations and mathematical theoretical frameworks are to theory formulation what methodology is to Big Data gathering and Big Data analytics. A lack of conceptual frameworks to address epistemological challenges of Big Data may slow progress in innovations and delay the development of Big Data's prospective applications according to recent reports and publications on Big Data. The book recalls the vigilance of Gaston Bachelard regarding the precision in making decisions based on the data alone.
Big Data's rapid growth has definitely created radical shifts with new opportunities, leading organisations and companies to shift their activities towards more data-driven decisions with the help of machine learning (ML) techniques and artificial intelligence (AI). A shift from stand-alone traditional desktop computing to embrace a more comprehensive strategy such as Mesh App and Service Architecture (MASA) strategy banking on Big Data to allow more dynamic connection of people, processes, things and services that are supporting today’s increasingly intelligent (AI) digital ecosystems, according to Gartner (2017).
The book refers to the different techniques and tools used to address the ambiguity and uncertainty as well as scaling challenges that helped to transform, analyse, and reveal hidden patterns. The identified patterns were used, in turn, successfully in the discovery process. The tools are presented, in this book, within the perspective that these tools will lend themselves to also scale as the technologies and methodologies evolve.
This book is about a new paradigm shift in the current analytics landscape where the edges and the fog are becoming the new interface for analytics insights and for hosting new artificial intelligence applications. According to Automation World: “Both edge analytics and fog analytics involve pushing intelligence and processing capabilities down closer to where the data originates.”
Excellent book! (Machine Learning)
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Fantastic book, Big Data of Things!
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