The Path to Predictive Analytics and Machine Learning

Chapter 1. Building Real-Time Data Pipelines

Discussions of predictive analytics and machine learning often gloss over the details of a difficult but crucial component of success in business: implementation. The ability to use machine learning models in production is what separates revenue generation and cost savings from mere intellectual novelty. In addition to providing an overview of the theoretical foundations of machine learning, this book discusses pragmatic concerns related to building and deploying scalable, production-ready machine learning applications. There is a heavy focus on real-time uses cases including both operational applications, for which a machine learning model is used to automate a decision-making process, and interactive applications, for which machine learning informs a decision made by a human.

Given the focus of this book on implementing and deploying predictive analytics applications, it is important to establish context around the technologies and architectures that will be used in production. In addition to the theoretical advantages and limitations of particular techniques, business decision makers need an understanding of the systems in which machine learning applications will be deployed. The interactive tools used by data scientists to develop models, including domain-specific languages like R, in general do not suit low-latency production environments. Deploying models in production forces businesses to consider factors like model training ...