By Anurag Sinha, Co-Founder & Managing Director, Wissen Technology
Banking and financial institutions are constantly on the hunt to devour more data from their customers to offer better experiences. And eventually, to tackle the volume of data, and its complexity, banks have started leveraging Machine Learning (ML) models to penetrate deeper into data troves and uncover insights.
According to a study, the global market for machine learning in the banking sector is expected to be worth over USD 21.27 billion by 2031.
Though a hot trend, leveraging ML is also becoming a complex affair for banks, especially after the COVID-19 pandemic increased the diversity of financial instruments in the booming digital economy. However, there is no need for banks to search for the next big tech to manage their data, at least for the next couple of years. Instead, banks need to adopt MLOps as a strategy to overcome key challenges in handling their growing data complexity.
What Is MLOps?
For beginners, MLOps can be called a derived variant of DevOps targeting AI and ML projects exclusively. It standardizes and automates processes impacted or affiliated with ML projects and establishes a high degree of flexibility, transparency, and governance for collaborative success in new deployments and integrations. Akin to its DevOps counterpart, MLOps enables enterprises to establish a culture of fast feedback on ML implementations by all teams involved.
Some of the major issues in digital outreach programs of banks arise predominantly due to inefficiencies in their underlying processes and workflows for digital transformation, especially those related to AI and ML initiatives. With MLOps, it becomes easier to establish a standard code of conduct and accountability through automated operations.
Let’s explore four ways in which MLOps can drive radical changes and help banks improve operational efficiencies:
Dynamic Scalability
The key trait that banking applications need to exhibit while being subject to ML initiatives is the free-hand scalability of their critical data infrastructure in minimal time. Traditional development environments often involve several bureaucratic layers marred with approval workflows.
With MLOps, the chain of approval complexities is eliminated through a highly flexible data infrastructure. There is minimal or no involvement from the IT function for upscaling data services and infrastructure. This proves to be a key factor in speeding up ML-driven initiatives and subsequently driving better efficiencies through more informed decision-making.
Automation
Inefficiencies in banking applications often occur due to delays and biased manual technology configurations involved in data-driven initiatives. Machine learning initiatives are no less different in this regard.
However, with MLOps, banks can automate the underlying process or workflow through which ML models are integrated into different application data streams. Furthermore, MLOps drives automation across the production landscape, including versioning, thereby establishing a solid path to successfully uncovering insights from any large dataset.
Cost-Effective Process Management
One of the biggest challenges that banks face while dealing with complex data initiatives is the cost involved, especially the costs they must incur in the event of a mishap or undoing a wrong configuration. With MLOps, there is a constant check on code quality, continuous feedback, collaborative peer reviews, and a high degree of accountability for every development task — just as DevOps does for modern agile software development projects.
As a result, continuous delivery and continuous integration become more streamlined with fewer quality issues. This ultimately lowers the running costs involved in new digital initiatives.
Governance
As banks replicate their ML models across multiple applications, there is a need to establish standards, protocols, and governance models for sharing of code, libraries, or even data models. Besides, there should be a traceable version control for all these digital assets that are leveraged by different ML models across applications.
MLOps brings on board this governance by establishing a common collaborative platform for different teams. Code, as well as libraries, are productized into reusable components by creators. These components, when leveraged by a different team, are automatically governed by the policies and best practices developed by the initial development team.
Summing Up
It is no secret that AI and ML innovations are driving considerable changes in the omnichannel business strategies for banks and financial institutions. However, it is important to establish an environment of trust before piggybacking ML technology on more customer-facing systems. This is where MLOps can prove to be a critical asset.
From promoting transparency and visibility into continuous integration and deployment of ML assets to bringing a pool of experts together through collaboration, MLOps can redefine efficiency in modern banking environments fueled by digital experiences.
If your banking channels are yet to experience the magic of MLOps, get in touch with us to have first-hand experience on revolutionizing your customer channels with data insights obtained like never before.
