Over time, companies across sectors have increasingly leveraged data to drive informed decision-making. The banking sector is no different. Financial institutions today harness massive data streams to develop powerful machine learning (ML) models, which are then deployed for specific purposes.
As data volumes grow and become more complex in nature, banks will need to rapidly scale ML models, lower operational costs associated with data management and address data challenges such as transparency and governance. This is how machine learning operations (MLOps) can help banks and financial institutions make a difference.
Banking as an MLOps use case
MLOps refers to a set of best practices that helps organizations efficiently and reliably deploy ML models to production. This ensures that there is room for continuous improvement on these models. The next question is how MLOps is critical to modern banking operations.
The proliferation of data science has not made it easier for data teams to work with ML models because, with data volumes growing, there is a constant need to replace old models with new models to ensure relevance. The larger an organization is, the more divisions and departments it has. Each of the departments may use different data management tools with their own siloed data storage systems that cannot be accessed by other departments.
The majority of banks also struggle significantly when it comes to inert model management, especially in environments where application deployments are slow and inefficient. MLOps, which essentially applies DevOps tools and methods to machine learning, can help banks address several of these challenges quickly and efficiently.
Almost a decade ago, DevOps simplified the way IT teams deployed applications and associated services by automating and standardizing the processes of application development, delivery and management. With DevOps, businesses were not only able to radically enhance application development processes but also improve overall quality and delivery timelines.
Given its similarities to DevOps, MLOps yields endless possibilities for banks in terms of revolutionizing the process through which ML models operate and deploy. By automating processes such as pipeline development and governance and making provisions for advanced data management tools, MLOps can foster an automated sequence where banks can seamlessly structure, deploy and manage ML models while gathering feedback at every stage of the process.
Cross-team collaboration is another aspect that makes MLOps critical for banks. Successful MLOps requires seamless collaboration between multidisciplinary teams, such as ML engineers, data scientists, financial analysts and IT operations. This can help eradicate silos within an organization and create scale, efficiency and long-term business value.
4 key benefits of MLOps
Seamless automation. With the help of MLOps, banks can automate the process through which AI/ML models are integrated within applications. This can be done seamlessly across all touchpoints and channels where consumers are digitally interacting with banks to enhance their overall experiences. Additionally, MLOps can also help with the automation of application versioning and drift while ensuring duplicability of similar results at scale.
Reduced cost. MLOps drastically mitigates the cost of AI/ML integrations within self-managed environments through traceability, version control, constant code checks and continuous integration/continuous delivery (CI/CD) pipelines.
Easy scaling. MLOps enables banks and financial institutions to develop highly agile and flexible application deployment infrastructure while allowing data teams to focus on critical tasks with minimal involvement of IT.
Effective governance. MLOps facilitates sharing code, allowing banks to reproduce application codes with traceable version control across a range of data modeling frameworks or libraries. By helping banks productionize models through rules-based automation, MLOps promotes automated deployments with easy AI/ML model governance.
Even today, however, MLOps is still ignored in sectors like banking until scalability becomes an issue. With time, as banks look toward increasing efficiency across critical customer touchpoints to drive satisfactory experiences, their inclination toward AI/ML and edge technologies will only increase. It is here that MLOps can fulfill the need for banks to manage, monitor and optimize their ML lifecycles.
About the author
Tanika Gupta is a principal data scientist at Sigmoid and is a specialist in machine learning solutions for the financial services industry with a passion for innovation. She has over 10 years of experience and has worked at leading banking and financial institutions across the globe. Three of her ideas on payment technology have been filed for patent protection. Previously she has worked for JPMorgan Chase & Co., Mastercard, American Express, HDFC Bank and GE HealthCare.