Building MLOps Pipelines in Fintech: Keeping Up with Continuous Machine Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V1I2P103Keywords:
MLOps, Fintech, Machine Learning, Credit Scoring, Fraud Detection, Continuous Integration, Continuous Delivery, Regulatory Compliance, Data Management, Cloud Computing, CI/CD Pipelines, Data Versioning Tools, Model Training Frameworks, Deployment Tools, Monitoring Solutions, Data Privacy, Model Governance, Data Lakes, Data Warehouses, Automated Testing, Real-Time Data ProcessingAbstract
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
References
[1] Chakraborty, S. (2018). Fintech: evolution or revolution. Business analytics research lab India.
[2] Marafie, Z., Lin, K. J., Zhai, Y., & Li, J. (2018, July). Proactive fintech: Using intelligent iot to deliver positive insurtech feedback. In 2018 IEEE 20th conference on business informatics (CBI) (Vol. 2, pp. 72-81). IEEE.
[3] Riemer, K., Hafermalz, E., Roosen, A., Boussand, N., El Aoufi, H., Mo, D., & Kosheliev, A. (2017). The Fintech Advantage: Harnessing digital technology, keeping the customer in focus.
[4] KOMANDLA, V. (2017). Overcoming Compliance Challenges in Fintech Online Account Opening. Educational Research (IJMCER), 1(5), 01-09.
[5] El Naqa, I., & Murphy, M. J. (2015). What is machine learning? (pp. 3-11). Springer International Publishing.
[6] Chen, Z., & Liu, B. (2018). Lifelong machine learning. Morgan & Claypool Publishers.
[7] Zenke, F., Poole, B., & Ganguli, S. (2017, July). Continual learning through synaptic intelligence. In International conference on machine learning (pp. 3987-3995). PMLR.
[8] Doya, K. (2000). Reinforcement learning in continuous time and space. Neural computation, 12(1), 219-245.
[9] Samuel, A. L. (1959). Some studies in machine learning using the game of checkers. IBM Journal of research and development, 3(3), 210-229.
[10] Duan, Y., Chen, X., Houthooft, R., Schulman, J., & Abbeel, P. (2016, June). Benchmarking deep reinforcement learning for continuous control. In International conference on machine learning (pp. 1329- 1338). PMLR.
[11] Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
[12] Talele, G. C. (2016). What Are The Key Areas Of ML-Ops/DL-Ops In Business Problems For Company Growth Using Cloud Environment?. Global journal of Business and Integral Security.
[13] Ashari, A., & Riasetiawan, M. (2015). High performance computing on cluster and multicore architecture. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(4), 1408-1413.
[14] Scutari, M., & Malvestio, M. (2014). Developing and Running Machine Learning Software: Machine Learning perations (MLOps). Wiley StatsRef: Statistics Reference Online, 1-8.
[15] Wang, X., Konishi, S., & Suzuki, T. (2009, April). Multi-Layer Optimized Packet Scheduling for OFDMA-based Cellular Systems. In VTC Spring 2009-IEEE 69th Vehicular Technology Conference (pp. 1-5). IEEE.
