Architecting production-ready AI platforms with open source frameworks

Most enterprise AI stalls at POC not because of model quality, but because of missing platform patterns. Drawing from experience enabling AI platforms across enterprise teams, Bhavik shares the open source stack that bridges experimentation to production.

Key takeaways:
• E2E workflow design with Kubeflow, Ray, and KServe, and pitfalls that break at scale
• Why naive GitOps fails for ML models and a safe promotion pattern with Argo CD
• AIOps with Apache Flink and LLM diagnostics to catch failures before incidents
• GPU scheduling and governance strategies that survived compliance reviews
• A modular platform roadmap grounded in the CNCF Platform Maturity Model

Attendees leave with a field-tested blueprint to stop AI projects from dying in staging.