When analyzing information for fraud detection, tasks must be run periodically. When building a fraud detection system, start by preparing the data, and work with small chunks of data and run parallel jobs so your machine learning (ML) models can predict fraudulent activity. For that, you schedule computer resources and, of course, the script. With AWS Batch, you only worry about your application job and run it at scale. With containers, you think in small processes and let AWS Batch run them concurrently. In this session, learn to build a fraud detection system and integrate it with other AWS services. This session is part of re:Invent Developer Community Day, a series led by AWS enthusiasts who share first-hand, technical insights on trending topics.