Building MLOps Pipelines in Fintech: Keeping Up with Continuous Machine Learning

Jayaram Immaneni

Abstract: In the fast-evolving world of fintech, integrating machine learning into business processes has transformed how financial services operate. However, as the demand for rapid deployment of machine learning models increases, the need for robust MLOps (Machine Learning Operations) pipelines becomes paramount. This paper explores the essential components of building effective MLOps pipelines tailored for the fintech landscape, emphasizing the importance of continuous integration and continuous deployment (CI/CD) in maintaining the relevance and performance of machine learning models. By leveraging automation and orchestration, fintech organizations can streamline the model development lifecycle, from data preparation and feature engineering to model training and evaluation. The discussion highlights the unique challenges fintech companies face, such as compliance with regulatory standards and the need for data privacy and security. It also underscores the significance of collaboration among data scientists, engineers, and business stakeholders to foster a culture of innovation and agility. Real-world examples demonstrate how leading fintech firms have successfully implemented MLOps practices to enhance operational efficiency, reduce time-to-market, and improve decision-making. Ultimately, this exploration aims to provide a comprehensive understanding of how fintech organizations can harness the power of continuous machine learning through well-structured MLOps pipelines, ensuring they remain competitive in a dynamic marketplace while delivering accurate and timely insights to their customers.

Keywords: MLOps, Fintech, Machine Learning, Credit Scoring, Fraud Detection, Continuous Integration, Continuous Delivery, Regulatory Compliance

How to Cite?: Jayaram Immaneni, "Building MLOps Pipelines in Fintech: Keeping Up with Continuous Machine Learning", Volume 9 Issue 3, March 2020, International Journal of Science and Research (IJSR), Pages: 1726-1734, https://www.ijsr.net/getabstract.php?paperid=SR20034093248, DOI: https://dx.doi.org/10.21275/SR20034093248