{"id":17111,"date":"2025-05-07T12:33:22","date_gmt":"2025-05-07T19:33:22","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=17111"},"modified":"2025-06-13T21:54:12","modified_gmt":"2025-06-14T04:54:12","slug":"supercharge-machine-learning-couchbase","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/supercharge-machine-learning-couchbase\/","title":{"rendered":"Supercharge Machine Learning (ML) Applications with Couchbase"},"content":{"rendered":"

Consider this scenario – you are a developer at a fintech company, and one of your users is notified to ask whether they had authorized an international transaction for $1,000. Instead of getting alarmed, the user clicks on the \u201cNo\u201d option, with the assurance that your company will take care of the rest.<\/p>\n

Behind the scenes, your fraud prevention AI application blocked the purchase before the response, only allowing it to go forward if the user selects \u201cYes\u201d. \u00a0The fraud detection system moves quickly, really quickly. It reviews the transaction and predicts whether or not it’s fraudulent with reasonable accuracy in under 50 ms.<\/p>\n

To make quick and accurate predictions, your system requires a highly performant, in-memory database such as Couchbase, which stores, retrieves, and delivers data like ML features, profiles, operational data, and other contextual information. Speed and flexibility like that are why leading fintech companies have chosen Couchbase.<\/p>\n

Revolut, for example, has built its fraud prevention application<\/a> to serve its 12+ million users. Although real-time fraud detection is a popular use case among Couchbase customers, we see customers leverage Couchbase for other high-speed use cases such as driver ETA, anomaly detection, dynamic pricing, forecasting, personalized promos, and many more. Couchbase Capella is a Database-as-a-Service (DBaaS) platform that simplifies the development of AI applications and reduces the operational overhead associated with data management.<\/p>\n

The role of a feature store<\/h2>\n

ML algorithms do not understand raw data, hence, it must be preprocessed (using a technique called feature engineering<\/a>) and transformed into features. ML features are the most relevant fields of a dataset to solve a prediction problem, such as fraud detection. A feature store, both online and offline (more on that in a bit), allows storage, reuse, and secure access to features.<\/p>\n

ML developers spend roughly 75-80% of their time on feature engineering. To reduce repeating this effort every time for model training, developers use a feature store to streamline their ML operations. The online feature store is used during predictive analysis, or inference, for low-latency serving of ML features to ML models. Before that can happen, the ML model needs to be trained, which is done with the offline feature store. It is used to store and manage features, which are created during the feature engineering stage.<\/p>\n

Several Couchbase customers use Apache Spark for feature engineering jobs because of its massive ecosystem of Python ML libraries for transformations and its MPP (massively parallel processing) capabilities.<\/p>\n

To accelerate the development of ML applications, we recently announced a few building blocks that allow you to leverage Capella as both an online and offline feature store within one unified platform:<\/p>\n

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    1. Feast plugins to leverage Couchbase in ML applications. Feast (short for Feature Store<\/i>) is a simple-to-use and cloud-agnostic open-source feature store. Many customers like Revolut already trust Capella as an online feature store. Capella Columnar<\/a> offers the analytics capabilities that are required for an offline feature store.<\/li>\n
    2. PySpark connector for Capella accelerates feature engineering by combining the massively parallel processing capabilities of Spark with the analytics capabilities of Capella Columnar. Feature engineering jobs involve processing relevant columns as opposed to entire rows and, therefore, can be accelerated using a columnar database like Capella Columnar.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      Feast plugins for Couchbase<\/h2>\n

      Feast<\/a> has been adopted by a wide variety of organizations<\/a> in different industries, including retail, media, travel, and financial services. The Feast plugins for Couchbase are available in the Feast project. The Capella-backed Feast online and offline feature stores offer the following benefits:<\/p>\n