Anjelica Ambrosio

With the rise in popularity of AI applications, enterprises dive headfirst into development without having the proper foundations in place. AI workloads require heavy resource usage, and few enterprises lack robust infrastructure to handle them efficiently. These AI workloads often involve sensitive data, large-scale data movement, and high-performance compute nodes that require secure communication between components. Typical network security in Kubernetes is no longer limited to isolating services. It now includes protecting model training pipelines, securing inter-node traffic, and enforcing policies that ensure data confidentiality and compliance. The most common challenges enterprises face while developing AI applications are overprovisioning resources, an old-school infrastructure setup (a VM-only mindset), and insufficient security to prevent cybersecurity risks and protect their data. 

In this session, we’ll walk through best practices for building optimal AI infrastructure while utilizing k0rdent and Kubernetes, and also leveraging Cilium to maintain stringent data security and compliance.

We’ll cover:

• Why over-provisioning resources is such a common error with AI infrastructure, and best practices for efficient use of resources for maximum ROI.
• How to move beyond a VM-only mindset to a more modern, Kubernetes/bare-metal–aware platform that can keep up with AI teams’ needs.
• How to safeguard AI workloads without sacrificing the scalability that makes Kubernetes effective in the first place.

With proper, strong infrastructure, AI workloads will run smoothly, securely, and without the usual operational overhead. Whether you’re a platform engineer, an AIOps Engineer or someone who wants to get into AIOps, you’ll benefit from this talk on best practices for creating optimal and secure AI infrastructure.