Serverless Data Pipelines for Scalable Financial ML Systems on AWS

Machine learning (ML) processes combined with serverless architecture, both on Amazon Web Services (AWS), have already proved to be a successful development of scalable, efficient, and cost-effective data pipelines, i.e., in the financial sector. The specified paper assumes the elaborate examination of the existing practices, frameworks, and approaches toward the implementation of serverless data pipelines to process financial ML systems. With the AWS services being used, i.e., Lambda, SageMaker, Glue, and Kinesis, financial institutions can now get real-time analytics, predictor models, and resource management on demand without incurring the operational overhead that they had to incur with traditional infrastructure. The architecture and the addition of ML, the characteristic of real-time analytics, the consideration of compliance, and the optimization of the cost of the serverless pipelines on the basis of AWS are critically checked. Moreover, it explores the idea of the resilience of the multi-cloud environment as well and points out the transformational aspect of AI in automated scaling and performance management. The article could be considered an illustration of the ways the implementation, functionality, and modifications of the financial ML applications in a cloud-native environment occur as a synthesis of existing literature and the market dynamics.