AI/ML

Federal agencies aren't struggling with AI due to lack of vision. According to a recent report, The Federal AI Readiness Gap, they're constrained by foundational gaps in data governance, infrastructure maturity, and architectural clarity that prevent scaling beyond pilots.

AI has shifted the baseline for infrastructure, from managing petabytes to operating seamlessly at exabyte scale. Agentic systems, long-context language models, simulation pipelines, and large-scale observability now demand storage architectures purpose-built for this data reality. Today we’re thrilled to announce ExaPOD, MinIO’s validated reference architecture for the exascale era: a one-exabyte, highly dense and efficient building block that

As a MinIO Curriculum Engineer, I spend a lot of time creating demos and learning environments for customers. These environments need to be realistic, comprehensive, and ready to showcase MinIO AIStor's capabilities in real-world scenarios. But here's the thing - setting up a proper demo environment manually can take hours of low-value, repetitive work. Let me

Government agencies must embrace AI responsibly, ensuring data sovereignty and security are top priority. To obtain sovereign AI, the right foundation must be in place to ensure infrastructure, data, and models remain fully controlled across agencies.

AIStor Tables brings Iceberg catalogs natively into on-prem object storage. It simplifies data organization, enforces table-aware security, and lets AI teams catalog unstructured assets in structured tables, thereby enabling discovery.