For nearly 50 years, banks have been using statistical models to facilitate lending decisions. Nowadays, almost every process in banking involves real-time Big Data analytics. However, decisions made on the basis of models are still far from perfect, and model operation management is one of the major problems for banks. How can MLOps help in solving this problem? But first, what exactly is MLOps?
What is MLOps?
Machine Learning Ops (or Model Ops) is a variation of DevOps. While DevOps focuses on process optimization when developing an application, MLOps is intended for analytics. Along with affecting predictive analytics, MLOps enables smooth, continuous, and efficient model development and deployment.
Due to the rapid adoption of Artificial Intelligence and Machine Learning, new models are emerging rapidly as well. Although many organizations recognize the importance of developing data culture, during continuous digital transformation, working with models is challenging, and the risk of errors is high.
The MLOps team helps to foster communication between Data Scientists, Data Engineers, application owners, and infrastructure owners. Also, MLOps coordinates flawless handoffs and execution so that models can move to the so-called "final mile". Workflow automation, version control, moving forward, compute resource management, monitoring, scaling, and customization are the responsibilities of MLOps.
Who needs MLOps?
Large banks have been using model analysis approaches for many years. At the same time, models are constantly changing, new ones are being introduced to replace outdated ones, and different departments use different methodologies, tools, and techniques to manage the life cycle of the model.
As a rule, banks isolate financial data in departmental repositories; working with models follows the same principle. A model that calculates risk in mortgage lending may miss data about the same person in the retail lending department. As a result, the model will not be able to take into account that the client already has a large loan and thus will make not the best decision.
In addition, launching new models at a large bank can take an unreasonably long time, since such organizations are less flexible. Deploying a new model can take several months. According to Gartner, less than half of the models developed end up being launched.
Young small banks and FinTech companies can find it easier to organize flexible work with analytical models. In addition, they don’t attract the close attention of regulators, since the volume of their operations doesn’t have a significant impact on the economy.
However, as the company grows, the regulatory burden is growing too. In the long-term, intuitive model management won't work. In order to compete with major players in the financial market, young banks need to strengthen their management and optimize their processes.
The difficulty of model management
In order to solve a problem quickly and effectively, one needs to understand its essence first. Speaking about banks and analytical models, the problem is, oddly enough, people.
On one hand, there is Data Science that is based on data analysis and aimed at increasing the efficiency of business processes using information management. On the other hand, there is a technical implementation that must provide a robust solution within regulatory and business constraints. The two systems operate in different ways, move at different speeds, and locally have different targets.
Only relatively recently, due to the spread of cloud technology development and the introduction of DevOps techniques, it has become possible to ensure synergy between the two systems. For example, the cloud can be used to keep the model lifecycle independent from a particular department. In addition, modern DevOps tools provide an opportunity for interaction between Data Scientists and technical specialists.
The future of MLOps
Regardless of who turns out to be more successful in the confrontation between banks and FinTech companies, MLOps will play an important role in the development of the industry over the next few years.
Almost all financial institutions now struggle against inert model management, especially against slow inefficient deployments. According to a McKinsey study, only 6% of companies actively use AI for decision-making, and less than 15% have an IT infrastructure to support model deployment.
At Andersen, we actively support DevOps integration into development processes and have expertise in working with AI and Data Science. Along with FinTech, we also develop solutions for eCommerce, Healthcare, and other industries.