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Extend ML capabilities to relational database-driven applications using AWS no-codelow-code solution

Organizations are looking at ways to improve the data stored in their relational databases and incorporate up-to-the-minute predictions from ML models. However, most of the ML processing are done offline in separate systems, causing delays in receiving ML inferences for use in applications. In addition, developing, installing, and integrating ML models require deep domain knowledge, technical skillset, and appropriate infrastructure. Join this session as we showcase how to extend ML capabilities via digital payment fraud prevention demo using Amazon Aurora ML integration with Amazon SageMaker. We explain how to train models, host endpoints and effectively incorporate real-time model inferences in your applications without any ML training. Download slide »

Speaker: Darshit Vora, Senior Startup Solutions Architect, AWS India