The path to predictive analytics and machine learning - Doherty, Conor (O'Reilly Media, 2017)
In many companies today, discussions about predictive analytics and machine learning tend to overlook one critical component: implementation. This report will help you examine practical methods for building and deploying scalable, production-ready machine-learning applications. Leveraging machine-learning models in production, after all, separates revenue generation and cost savings from mere intellectual novelty. Product specialists from MemSQL describe several real-time use cases, including "operational" applications, where machine-learning models automate decision-making processes, as well as "interactive" applications, where machine learning informs decisions made by humans. You'll also explore modern data processing architectures and leading technologies available for data processing, analysis, and visualization. With this report, you'll find ways to: Build real-time data pipelines Process transactions and analytics in a single database Create custom real-time dashboards Redeploy batch models in real time Build real-time machine learning applications Prepare data pipelines for predictive analytics and machine learning Apply predictive analytics to real-time challenges Use techniques for predictive analytics in production Move from machine learning to artificial intelligence