Abstract
AI systems are only as powerful as the data foundations that support them. As applications increasingly rely on vectors, tables, graphs, and long-lived datasets, object storage must evolve to do more than simply hold data—it must actively support how AI applications coherently access, move, and reuse it over time.
In this webinar we will examine the architectural patterns shaping modern AI-ready object storage. We will explore how object storage is designed to support emerging AI data types alongside traditional unstructured data, while remaining scalable, durable, and cost efficient at cloud scale. Central to this evolution is tighter integration between data lifecycle management and application workflows, enabling data to flow seamlessly from ingestion and training to inference, governance, and long-term retention.
Attendees will gain practical approaches to building AI-ready storage systems that deliver scale, efficient access, and long-term data reuse.