St. Petersburg Bank, together with GlowByte, has increased the efficiency of the modeling platform using new MLOps tools

2024: Implementation of MLOps tools to improve the performance of the modeling platform

Bank St. Petersburg, with the support of integrator GlowByte, has introduced MLOps tools to improve the efficiency of the modeling platform. GlowByte announced this on January 13, 2025. The project made it possible to speed up the cycle of updating models, centralize the processes of preparing data, training models and their deployment, as well as implement an automated system for controlling the versions of model code.

As part of the project, Airflow, Gitlab CI/CD and DVC (Data Version Control) tools were introduced, thanks to which the lifecycle of models is managed. Airflow provides task coordination and automatic start-up of processes, using the Gitlab CI/CD tool, a single pipeline of model output from the development stage to production is built, DVC allows you to save data to S3, as well as versioning large data files, models and other artifacts.

We integrated all stages of development and testing with the GitLab version control system, thanks to which we automated the assembly processes for models machine learning and the process of model production, made the history of all changes in the code base transparent. In addition, Airflow made it possible to automate the launch of batch models, and also made it possible to optimize the management of cluster resources, "explained KubernetesSergey Novoselov, architect of Advanced Analytics GlowByte.

The introduction of MLOps approaches increased interest in using the ML platform, which, in turn, contributed to attracting more business users at St. Petersburg Bank. 

In the bank "St. Petersburg" ML-platform has existed for a long time. In 2024, thanks to joint work with GlowByte, it received an additional impetus for development on the way to a single bank space to solve complex multifactorial problems. The implemented tools will not only increase the efficiency of model developers, but also greatly facilitate the availability of their results to users, said Kirill Svetlov, Managing Director of the Treasury Directorate of PJSC Bank St. Petersburg. "

In the future, it is planned to continue the development of the platform, improving the stability of its operation by better managing resources in the Kubernetes cluster and applying modern approaches in the field of machine learning and MLOps, including the MLFlow tool for tracking experiments with models.