Sovereign AI architecture: Scaling distributed training with Kubeflow Trainer and Feast on Red Hat OpenShift AI

Sovereign AI architecture: Scaling distributed training with Kubeflow Trainer and Feast on Red Hat OpenShift AI User story: The dilemma of "AI independence" 3 pillars of sovereign AI Technical sovereignty (the foundation) Data sovereignty (the asset) Technical solution The open blueprint for AI sovereignty: Red Hat AI Integrated compute: Kubeflow Trainer Sovereign data: Feast Feature Store Completing the lifecycle: Sovereign model serving Architecture Wrapping up Ready to build your own sovereign AI factory? The adaptable enterprise: Why AI readiness is disruption readiness About the author Umberto Manganiello More like this Context as architecture: A practical look at retrieval-augmented generation Red Hat Enterprise Linux now available on the AWS European Sovereign Cloud Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share As AI becomes an engine of national competitiveness, the concept of sovereign AI—the capacity to operate AI systems free from external influence—is increasingly relevant, but the path to adoption is filled with challenges. A recent survey of over 900 IT leaders and AI engineers about AI adoption exposes a significant "value gap," showing that, despite high enthusiasm (72%), only 7% of Europe, the Middle East, and Africa (EMEA) organizations are delivering results. The survey highlights that data privacy and infrastructure silos are paralyzing AI development efforts. As a result, sovereign AI has rapidly moved from being a theoretical "cloud challenge" into a practical necessity. By mitigating the specific risks identified in the Red Hat survey, sovereign AI allows regulated enterprises to move confidently from pilot to production without compromising on: Regulatory compliance : Adherence to strict regulations like General Data Protection Regulation (GDPR), the EU AI Act, and data residency laws that mandate citizen data remain within specific borders. Operational resilience : The ability to continue operations during geopolitical instability or disconnection from the global internet. Strategic autonomy : Organizations avoid vendor lock-in and maintain full control over the intellectual property, such as models and weights, generated from sensitive data. Red Hat OpenShift AI provides a foundation for this sovereignty, enabling organizations to build an "air-gapped" AI factory while maintaining absolute control over security, data, models, and results. In this article we look at specific examples of sovereign AI challenges our clients are facing, abstract the main themes that need to be addressed, and propose a solution for these problems. The protagonist : Dr. Aris (a composite persona based on real customer challenges), chief data officer for the Ministry of Health in a mid-sized European nation. The challenge : The Ministry possesses a goldmine of data, decades of anonymized patient records, genomic sequences, and local epidemiological history.