
Enterprise developers are increasingly finding that moving massive datasets to external machine learning models creates unacceptable latency and security risks for real-time applications. The traditional architecture, which relies on extracting data from a relational database and sending it to a separate inference engine, often leads to a fragmented infrastructure that is difficult to scale and maintain. To address these systemic










