Chapter 14. New generation data architectures for Machine learning

This is our last chapter, and we will take a detour from our usual learning topics to cover some of the solution aspects of Machine learning. This is in an attempt to complete a practitioner's view on the implementation aspects of Machine learning solutions, covering more on the choice of platform for different business cases. Let's look beyond Hadoop, NoSQL, and other related solutions. The new paradigm is definitely a unified platform architecture that takes care of all the aspects of Machine learning, starting from data collection and preparation until the visualizations, with focus on all the key architecture drivers such as volume, sources, throughput, latency, extensibility, data quality, reliability, security, self-service, and cost.

The following flowchart depicts different data architecture paradigms that will be covered in this chapter:

The topics listed here are covered in depth in this chapter...