At the TsIPR conference in Moscow, Evgeniy Garanin, VP for Digitalization and IT at TVEL (Rosatom’s fuel division), argued that Russian industry needs a centralized operational environment to manage AI assets at scale. Speaking about the upcoming AtomMind 2.0 platform—developed jointly with Industry Soft Solutions—Garanin described it as a response to fragmented AI deployments that stall between pilot and production.

AtomMind 2.0, slated for November release, targets two bottlenecks: the speed of model development and the operational friction of moving from test to full-scale deployment. The platform is designed to unify data consolidation, model monitoring, and predictive maintenance for industrial equipment. Rather than a one-size-fits-all solution, it emphasizes modular integration with existing plant systems, reducing the barrier to entry for factories with legacy infrastructure.

A notable engineering decision is the partnership ecosystem. TVEL and Industry Soft Solutions brought in Arenadata, a major infrastructure provider, to handle data layer scalability. This suggests a deliberate separation of concerns: AtomMind handles the AI orchestration layer, while Arenadata provides the distributed storage and compute backbone. The trade-off is clear—faster time-to-market and vendor specialization versus tighter coupling and potential lock-in.

Alexey Akulov, CEO of Industry Soft Solutions, highlighted that the ecosystem model shortens feedback loops between customers and developers. For data engineers, the takeaway is pragmatic: AtomMind 2.0 is less a revolutionary AI platform and more a well-architected bridge between industrial data sources and production ML pipelines.

Source: RIA Novosti