Data Product Architectures: O’Reilly Webinar
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.