Chronon — A Declarative Feature Engineering Framework

A framework for developing production grade features for machine learning models. The purpose of the blog is to provide an overview of core concepts in Chronon. <a href="https://www.linkedin.com/in/nikhilsimha" rel="noopener ugc nofollow" target="_blank">Nikhil Simha Raprolu</a> <h1>Background</h1> Airbnb uses machine learning in almost every product, from ranking search results to intelligently pricing listings and routing users to the right customer support agents. We noticed that feature management was a consistent pain point for the ML Engineers working on these projects. Rather than focusing on their models, they were spending a lot of their time gluing together other pieces of infrastructure to manage their feature data, and still encountering issues. One common issue arose from the log-and-wait approach to generating training data, where a user logs feature values from their serving endpoint, then waits to accumulate enough data to train a model. This wait period can be more than a year for models that need to capture seasonality. This was a major pain point for machine learning practitioners, hindering them from responding quickly to changing user behaviors and product demands. <a href="https://medium.com/airbnb-engineering/chronon-a-declarative-feature-engineering-framework-b7b8ce796e04">Visit Now</a>