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All digests tagged Kubernetes
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Learn how to deploy NVIDIA Dynamo on Kubernetes for scalable AI inference. We cover the Dynamo Operator, DGDR/DGD paths, and the request flow from OpenAI-compatible front ends to engines like vLLM.
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Transitioning AI agents to production requires a governed framework. Learn how to implement centralized MCP Gateways, LangGraph workflows, and robust CI/CD pipelines for secure, scalable, and observable enterprise AI.
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Robert Brennan details four strategies for sandboxing AI coding agents: Docker, GitHub Actions, VMs, and Kubernetes. He argues that Kubernetes offers the best isolation and scale for autonomous agents.
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Codex automates production monitoring, diagnosing checkout failures in Grafana and resolving Kubernetes OOM kills. Learn how AI agents accelerate incident response and integrate security checks.
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Discover how CoreWeave builds an end-to-end AI platform, integrating purpose-built GPU infrastructure, LOTA storage, and Mission Control™ for reliable, large-scale AI training and inference.
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NVIDIA OpenShell 0.1 provides a secure runtime boundary for AI agents, preventing compromised agents from executing unauthorized actions. It allows developers to deploy agents in a sandboxed environment where all components inherit consistent policy, credential, and audit controls.
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Inference workloads are becoming foundational infrastructure, requiring a dedicated control plane. Optimization must shift from cost per token to cost per successful task, managing complex distributed transactions across multiple hardware and software layers.
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To run agents as users, we leverage Kubernetes namespaces per person in every region. A sidecar mounts the identity into the pod, allowing automated jobs to operate with full user access.
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Learn how KV Cache-Aware Routing and P/D Disaggregation optimize LLM inference for agentic workloads. Discover strategies to reduce P99 latency from 900ms to 100ms using advanced Kubernetes patterns.
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To train Krea 2, we built a system that handles crashes at scale and uses advanced metrics like tensor core utilization and InfiniBand monitoring to ensure stability.
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