This text discusses how a fintech company's developer can use Couchbase as a highly performant, in-memory database to build a fraud prevention AI application. The system uses a feature store called Feast, which allows storage, reuse, and secure access to features for machine learning (ML) algorithms. To accelerate the development of ML applications, Couchbase offers several building blocks, including the PySpark connector for Capella, which combines the massively parallel processing capabilities of Spark with the analytics capabilities of Capella Columnar. These building blocks enable developers to streamline their ML operations, reduce data leakage, and decouple ML from data infrastructure, ultimately reducing latency in serving ML models. The system also provides a unified platform for online and offline feature stores, allowing developers to leverage Capella as both an online and offline feature store within one platform.