Platforms for the AI Era

Driven by our CNCF roots and a mission to help the platform community thrive, we are excited to announce our next meetup—a joint event proudly hosted in collaboration with Nutanix, Mirantis, and Nirmata.

Welcome to the Platform Engineering AI Meetup, happening on August 27th, 2026, at the Nutanix Office in San Jose!

RSVP here: https://luma.com/eavisvo3
(Only 180 seats, a few seats left)

Due to the overwhelming community response and interest, we are squeezing in 6 talks, 15 minutes each.

​Agenda:

• 5:30 - 6:00: Welcome and Registration
• 6:00 - 6:45: Talks 1 -3 (details below)
• 6:45 - 7:15: Break & Networking
• 7:15 - 8:00: Talks 3 -6 (details below)
• 8:00 - 8:30: Wrap-up and Networking

Talks:

1. Distributed LLM Inference at Scale (Nutanix): As LLMs continue to grow in size, efficient distributed inference has become essential for production deployments. This talk covers the key architectural principles
2. Your Golden Paths Weren't Built for Agents, (Massdriver): Agentic tooling collapsed the cost of producing infrastructure changes, and three things ops depends on won't survive it. Access control that binds to resource identity, once infra gets created and destroyed faster than anyone tracks. State that confirms what you have but can't predict what a change would do. And dependency and provenance knowledge that was never stored anywhere queryable, because a person always knew.
3. Introducing OttoFlow: Deterministic AI Workflows for Kubernetes (Nirmata): Most AI projects fail due to unpredictable reasoning, runaway costs, and security risks. OttoFlow addressed these core issues by shifting from open-ended prompts to deterministic, Kubernetes-native workflows that prioritize strict governance. It turns AI from a risky experiment into a reliable, production-grade tool for complex cluster operations. Learn how you can use OttoFlow, a new agentic workflow framework from the creators of Kyverno, to address core platform engineering challenges.
4. Building a Self-Service Data Platform for Telco-Scale Identity and Ingestion (T-Mobile): Legacy telecom architectures struggle with massive data growth, making cloud-native platform modernization an operational necessity. This session highlights a data platform processing three billion daily records that slashed total processing time from 60 hours to 3.5 hours. Using graph-based modeling and a self-service pipeline framework, the system resolves fragmented customer identities while cutting cloud costs by 90%. Attendees will walk away with practical design patterns for distributed ingestion, lakehouse storage, and pipeline governance at enterprise scale.
5. Platform in the AI Infrastructure era: A Tryst with k0rdent (Mirantis): In the AI infrastructure era, your platform addresses the complexity of deploying large-scale AI and GPU workloads. This talk presents k0rdent, an open-source, Kubernetes-native distributed container management platform. It functions as a unified control plane to automate bare-metal discovery, GPU lifecycle management, and scalable model inference across multi-cloud and edge environments.
6. Building AI-Powered Platforms at Scale: Lessons from Real-Time Fraud Detection Systems (PayPal): This session explores how an internal platform powers real-time AI fraud detection across one billion daily transactions. Golden paths, resilience patterns, and self-service tools keep decisioning under 100 milliseconds while evaluating over 200 signals per transaction. These investments reduced fraud by 76% and false positives by 92% at 50,000 transactions per second. Platform engineers will gain practical insights on scaling high-stakes AI workloads safely in production.

To reserve your spot RSVP at: https://luma.com/eavisvo3