Description:

Data products derive their value from data and generate new data in return. As a result, machine-learning techniques must be applied to their architecture and development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product.

Data product architectures are, in effect, life-cycles. Understanding the data product life-cycle enables architects to develop robust, failure-free workflows and applications. Benjamin Bengfort discusses the data product life-cycle and outlines the Lambda Architecture, demonstrating how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Benjamin then explores wrapping a central computational store for speed and querying and covers monitoring, management, and data exploration for hypothesis-driven development. From web applications to big data appliances, this architecture serves as a blueprint for handling data services of all sizes.

About Benjamin Bengfort

Benjamin Bengfort is a Data Scientist who lives inside the beltway but ignores politics (the normal business of DC) favoring technology instead. He is currently working to finish his PhD at the University of Maryland where he studies machine learning and distributed computing. His lab does have robots (though this field of study is not one he favors) and, much to his chagrin, they seem to constantly arm said robots with knives and tools; presumably to pursue culinary accolades. Having seen a robot attempt to slice a tomato, Benjamin prefers his own adventures in the kitchen where he specializes in fusion French and Guyanese cuisine as well as BBQ of all types. A professional programmer by trade, a Data Scientist by vocation, Benjamin's writing pursues a diverse range of subjects from Natural Language Processing, to Data Science with Python to analytics with Hadoop and Spark.