ML Model inference Scaling on Kubernetes Using Horizontal Pod Autoscaling and Rolling Updates
Make ML Models running on Kubernetes survive a traffic spike, and ship a new model version without a single failed prediction
Kubernetes103
Check out the latest posts
Kubernetes ConfigMaps, Secrets, and Persistent Volumes for ML Models
Managing Application Configuration, Credentials, and Persistent Model Storage in Kubernetes
Deploying ML Models on Kubernetes
From Docker Containers to Production-Ready Kubernetes Deployments
Introduction to Kubernetes for MLOps Engineers
Kubernetes core concepts and architecture
Project: Multi-Model AI on Kubernetes
A complete Kubernetes project showing how to deploy, route between, auto-scale, and monitor multiple specialized AI
Project: OmniGate LLM
One OpenAI-compatible endpoint to unify, route, cache, load-balance, and manage requests across multiple LLM providers with built-in failover, rate limiting, and production-ready observability.