#kubernetes
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Building and Scaling Multi-Agent AI Systems on GKE
A practical guide to deploying AI agents on GKE, using the Model Context Protocol for infrastructure troubleshooting, and implementing secure sandboxing for AI-generated code.
Google Cloud TechInfrastructure for Large-Scale Model Training and Inference
To train models at scale, treat hardware failures as inevitable, prioritize metrics over dashboard status, and use automated scheduling to fluidly move production inference between internal clusters and external providers.
AI EngineerKubernetes vs. OpenShift: Platform Engineering Trade-offs
Kubernetes provides the raw container orchestration engine, while OpenShift offers an opinionated, integrated platform that bundles CI/CD, security, and management tools to reduce operational overhead.
IBM TechnologyNVIDIA Dynamo Snapshot: Reducing AI Inference Cold-Start Latency
NVIDIA Dynamo Snapshot uses CRIU and custom CUDA checkpointing to bypass slow inference cold-starts, enabling near-instant scaling of AI workloads on Kubernetes by restoring pre-warmed model states.
Standardizing AI Agent Deployments with Containers
By containerizing AI agents like OpenClaw, teams can move from inconsistent local setups to reproducible, secure, and scalable deployments across local machines, Kubernetes, and OpenShift.
AI EngineerScaling AI Agent Workflows with ACP and Kubernetes
Onur Solmaz explains how to automate high-volume PR processing using the Agent Client Protocol (ACP) and disposable Kubernetes-based agent environments to handle hundreds of daily contributions.
AI EngineerGitOps and ArgoCD: Principles and Architecture
GitOps uses Git as the single source of truth for infrastructure, employing pull-based agents like ArgoCD to continuously reconcile the live state of a Kubernetes cluster with the desired state defined in code.
Scaling TPUs on GKE for Massive AI Workloads
GKE treats TPU slices as atomic units for seamless scaling up to 9k+ chips, with flexible capacity like DWS Flex/Calendar and custom fallbacks for cost-efficient ML training/inference.
Google Cloud TechShowing 8 of 8