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Building AI infrastructure tools in the open. These projects aim to solve real problems in the ML/AI platform space and are designed for potential CNCF contribution.
Kubernetes-native AI inference gateway for multi-model routing, A/B testing, and intelligent failover. Features circuit breakers with exponential backoff, OpenTelemetry tracing, smart routing (cost/latency/context-length), and configuration hot-reload. CNCF Sandbox candidate.
Production-ready cost optimization platform for AI/ML workloads. Features GPU utilization monitoring, budget forecasting with alerts, ML-based anomaly detection, automated right-sizing recommendations, and multi-cloud billing integration. All 3 phases complete.
Production-ready multi-cloud MLOps platform on AWS EKS, Azure AKS, and GCP GKE with defense-in-depth security. Enables data science teams to deploy ML models and LLMs from experimentation to production in 15 minutes with full auditability, drift detection, and GitOps-driven infrastructure.
GPU compute price aggregator — "Trivago for ML training". Arbitrages spot pricing across AWS, RunPod, and Lambda Labs to find the cheapest GPU instances for batch training jobs.
Docker Compose for AI Agents — Declarative spec that deploys AI agent stacks to Kubernetes. GitOps-native with Kortex integration for inference governance. Transparent abstraction: generates readable K8s manifests you own.
Contributions to CNCF and other open source projects coming soon.
Currently focused on building these projects to production-ready status before contributing upstream.
Complete MLOps platform showing how all the pieces fit together.
Check out my GitHub profile for more projects and contributions.