Kubernetes for AI
Understand how Kubernetes architecture, persistent storage, metrics, and resource inspection affect ML infrastructure.
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Kubernetes architecture for ML workloads
Understand control planes, workers, services, and storage.
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What is sovereign AI? Control across the AI lifecycle
Define and verify control over AI data, models, compute, operations, providers, and recovery.
Run ML workloads on your existing Kubernetes cluster
Plan workload ownership, GPU access, storage, and recovery on an existing cluster.
Docker build caching for ML workloads on Kubernetes
Understand which caches accelerate image builds, node pulls, and model initialization.
What are your ML jobs connecting to?
Trace registry, Git, data, model, and artifact traffic to the component making each request.
GPU jobs stuck Pending on Kubernetes: a debugging guide
Diagnose GPU resource availability and the constraints that prevent placement.
GPU sharing on Kubernetes: MIG vs. time-slicing
Compare MIG instances and shared GPU access before configuring workloads.
Run batch LLM evaluations on Kubernetes
Design evaluation workers that tolerate retries and produce complete results.
Run Promptfoo evaluations on Kubernetes with Polyaxon
Package an evaluation suite and submit it as a Polyaxon job.
Compare platforms
Apply the concepts above to a documented platform decision, including where each option fits and when they can coexist.