CNCF-hosted Co-located Events Europe 2025

Organizations face a critical challenge: data is growing exponentially across distributed locations, but traditional centralized processing approaches are becoming unsustainable.

Specifically, the challenges of massive data transfer costs, regulatory compliance issues, and network reliability problems are slowing the adoption of ML infrastructure. This is particularly acute in scenarios like processing data from distributed deployments or analyzing real-time sensor edge data.

This talk demonstrates implementing compute over data with Kubernetes - bringing ML compute to where data is being created. Using real-world examples, including an energy company managing 15,000 microgrids, we demonstrate processing data in place, ensuring regulatory compliance, reducing costs, and improving reliability while maintaining centralized control. We'll showcase implementation using open-source tools like Bacalhau, providing attendees with practical patterns for modernizing their ML infrastructure.