A modern dilemma: When to use a rules engine versus machine learning
Who is this presentation for?
- Architects and engineers
Machine learning is taking the world by storm, and many companies with rules engines in place for making business decisions are starting to leverage it. However, the two technologies are geared toward different problems. Rules engines are used to execute discrete logic that needs to have 100% precision; machine learning is focused on taking a number of inputs and trying to predict an outcome.
Andrew Bonham details the strengths of both rules engines and machine learning and identifies the best use cases for each. You’ll also learn patterns for using rules and machine learning together. For example, you can run a machine learning model and use the output as an input into rules. The inverse can also be true, where the output of rules is a feature input into a machine learning model. Then Andrew leads a demo of one the patterns using a rules engine with a machine learning model.
Join in to learn when to apply each of these technologies—and to which problems.
What you'll learn
- Understand the key differences between rules and machine learning
- Learn when to use rules and when to use machine learning
Andy Bonham is a distinguished engineer at Capital One with over 18 years of experience building real-time high-transaction decisioning and workflow applications. His application architecture areas of expertise include microservices, reactive architecture, case management, BPMS products, rules engines, imaging solutions, and enterprise common capabilities. He holds both an undergraduate and a master’s degree in computer science with a concentration in information security as well as several industry certifications, including AWS Certified Solutions Architect – Associate, CISSP, and CSSLP. He recently spoke at the Red Hat and Reactive Summit conferences and has written a number of microservice articles for Capital One Tech, published on Medium.
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