This post demonstrates how to build a real-time machine learning system using Tecton and Databricks, simplifying the challenges of building operational machine learning systems that require real-time data. By leveraging Tecton's feature platform and Databricks' MLflow integration, teams can create a model serving endpoint in 15 minutes or less, including real-time data processing and online inference. The system is designed to handle high latency constraints and ensure feature freshness, making it suitable for applications that require fast and accurate predictions. By using Tecton and Databricks, developers can build real-time ML systems without months of manual engineering work.