<strong>Chronon — A Declarative Feature Engineering Framework</strong> is a blog post providing an overview of core concepts in Chronon, a framework designed for developing production-grade features for machine learning models. It addresses pain points in feature management encountered by ML Engineers, such as the log-and-wait approach for generating training data and training-serving skew. Chronon allows defining features, centralizing data computation for both model training and production inference, and ensuring consistency between the two. The post focuses on the Chronon API and capabilities, including ingesting data from various sources, transforming data, producing results online and offline, updating results flexibly, and using a powerful Python API. It also explains the concepts of accuracy, data sources, computation contexts, computation types, and aggregations in Chronon.