HSE Develops Its Own MLOps Platform

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HSE researchers have developed an MLOps platform called SmartMLOps. It has been created for artificial intelligence researchers who wish to transform their invention into a fully-fledged service. In the future, the platform may host AI assistants to simplify educational processes, provide medical support, offer consultations, and solve a wide range of other tasks. Creators of AI technologies will be able to obtain a ready-to-use service within just a few hours. Utilising HSE’s supercomputer, the service can be launched in just a few clicks.

Many researchers working in AI development lack sufficient experience in building web services, managing distributed computing resources, system administration, information security, and deployment process automation. SmartMLOps was created to help eliminate the need to learn additional technologies and to address issues of repeatability and reproducibility in machine learning experiments.

The project partners include the HSE AI Research Centre, the School of Software Engineering at the HSE Faculty of Computer Science, and the HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM). Experienced and highly qualified HSE specialists are involved in the development and deployment of the MLOps platform.

According to the project lead, Hadi Saleh, the platform is already undergoing pilot testing. Its primary goal is to reduce the time and resources required to deliver product-level services. SmartMLOps ensures the necessary level of reliability, security, and transparency in the use of AI technologies. It allows developers to save time on non-core activities such as system administration, DevOps, and CI/CD. The platform provides a unified graphical interface through which users can create a repository with an AI module template, access models, a monitoring system, orchestrators, and the supercomputer, as well as track their services’ metrics and promptly address issues.

To create a multifunctional, user-friendly, and modern service, the platform’s developers tackled the complex challenge of integrating HSE’s cHARISMa supercomputer cluster into cloud computing. The platform is based on a microservice architecture, with the main component being a framework developed by the HSE AI Research Centre.

Hadi Saleh

‘The platform's functionality includes the creation, storage, versioning, training and fine-tuning of AI models; the deployment of ready-made models as product services with REST APIs; the construction of training pipelines; the monitoring of services and the overall state of computing resources; the ability to use data from HSE’s corporate information systems; and access to cloud storage and supercomputer resources. The framework itself and the first AI modules have already been deployed, and analytical dashboards for monitoring the platform’s status have been implemented. A special autoscaling technology was developed and introduced to save resources,’ said Hadi Saleh.

Unlike commercial solutions such as Amazon SageMaker or Google AI, SmartMLOps is primarily aimed at researchers and students at HSE. To gain access, they simply need to submit an application and await approval. Until the end of 2025, anyone interested may take part in the platform's pilot use.

A special ETL process was developed to securely extract and anonymise information from HSE’s corporate systems. This helps in analysing student behaviour and building predictive models, for example, to forecast career prospects.

Elena Kozhina

‘We expect that the implementation of the SmartMLOps system will mark a significant milestone in accelerating the development of AI-based services at our university,’ noted Elena Kozhina, Deputy Vice Rector and project curator.

At present, eight AI models have been deployed on the platform, and integration has been carried out with three of HSE’s corporate information systems. Two subsystems of the MLOps platform, developed with the involvement of specialists from the Faculty of Computer Science and MIEM, have been registered with the Russian Federal Service for Intellectual Property (Rospatent).

Sergey Lebedev

‘The development of such an ambitious and promising infrastructure project posed a considerable challenge and a timely necessity for the university. SmartMLOps enables the simultaneous solution of research, educational, and administrative tasks of various scales and levels. We managed to form a unique team of professionals and involve students and research assistants in the process,’ said Sergey Lebedev, Head of the School of Software Engineering at the HSE Faculty of Computer Science.

Anton Sergeev

Anton Sergeev, Director of the Centre for Software Development and Digital Services at HSE MIEM, emphasised that systematic work with AI models is impossible without MLOps platforms, which support the full lifecycle of models—from data import and cleaning to controlling the outcomes. ‘Such high-tech projects set a benchmark for AI leadership not only within our university, but across the country as a whole. They represent HSE’s scientific excellence in the form of a system that will assist ML specialists in applying AI models to a wide range of real-world problems,’ he said.

Pavel Kostenetskiy

Pavel Kostenetskiy, Head of the Supercomputer Modelling Unit, gave the service high praise. ‘The MLOps platform will make it easier for HSE researchers working in AI to access the ‘cHARISMa’ supercomputing cluster and will lower the entry threshold into high-performance computing. In a sense, HSE’s supercomputer will appear like a cloud within this MLOps platform, which many will find convenient,’ concluded Pavel Kostenetskiy.