Machine Learning Ops in Quantitative Finance: Opportunities and Challenges

Abstract

Machine Learning Operations (MLOps), which involves data analytics techniques, are being utilized more in Quantitative Finance to accelerate the rate of decision-making, automate and improve the process of decision-making in real-time. ML enables analysis of large databases in real-time, thus ensuring the increased activity and the better controlled risk situations. Automation allows making business processes more advanced and responsive to market requirements. The machine learning's capability to adjust on the current market variables will result in progress that never ceases. But there are challenges including reliability, quantity and quality of the data, and managing risks. Ultimately, it is vital to view the challenges and possibilities alongside ML implementation. The present paper investigates the technological advancements in the process and the opportunities as well as the restrictions linked to quantum computing in relationship to financial management.  Special attention should be paid to the revolutionary changes induced by these technologies as well as to the importance of strategic integration and innovation.