Turn LLM experiments into a reusable engineering knowledge base
Use Polyaxon run queries, comparison dashboards, artifact reports, and lineage to preserve the evidence behind LLM engineering decisions.
LLMOpsGuides
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Kubernetes objects for ML platform teams
Understand Kubernetes objects, desired state, metadata, ownership, and reconciliation through the lifecycle of an ML workload.
KubernetesGuides
Managed Prometheus for ML platforms
Decide between managed and self-operated Prometheus using scale, availability, PromQL compatibility, data governance, cost, and operational ownership.
MonitoringKubernetesMLOps
BTF for portable eBPF observability
Understand how BPF Type Format supports eBPF introspection, CO-RE portability, safer deployment, and kernel-level observability for ML infrastructure.
KubernetesObservabilityMonitoring
Debug Kubernetes ML workloads
Debug ML workloads by following Kubernetes admission, scheduling, Pod startup, containers, nodes, networking, storage, and application outcomes in order.
KubernetesMLOpsGuides
How to use Kubernetes ephemeral volumes and storage
A guide to Kubernetes ephemeral storage options, emptyDir, CSI ephemeral volumes, generic ephemeral volumes, and monitoring storage pressure.
KubernetesGuides
Kubernetes deployments: a complete guide
Learn how Kubernetes Deployments manage pods and ReplicaSets, support rollout strategies, and keep applications available.
KubernetesGuides
OpenTelemetry zero-code instrumentation
Use OpenTelemetry automatic instrumentation as a safe baseline, then add domain spans, stable attributes, sampling, and rollout controls for ML services.
ObservabilityMonitoringMLOps
Kubernetes cost monitoring for ML workloads
Allocate Kubernetes and GPU costs to ML teams and runs while keeping idle capacity, shared services, failed work, data movement, and useful outcomes visible.
KubernetesMLOpsMonitoring
Optimize LLM performance and cost with controlled experiments
Track usage and pricing assumptions in Polyaxon, compare cost against quality in run dashboards, and control resources and concurrency during LLM experiments.
LLMOpsEvaluationGuides
Distributed tracing for ML platforms
Trace ML requests and operations across APIs, queues, schedulers, storage, model services, and external tools without losing context or causality.
ObservabilityMonitoringMLOps
Kubernetes admission controllers for ML platforms
Use built-in admission, policies, and webhooks to enforce safe ML workload defaults without adding unnecessary latency or a cluster-wide failure point.
KubernetesMLOpsGuides
Kubernetes learning projects for ML engineers
Learn Kubernetes through practical ML projects covering Pods, Jobs, storage, networking, scheduling, observability, security, and failure recovery.
KubernetesGuides
Istio vs. Linkerd vs. Consul for ML platforms
Compare Istio, Linkerd, and Consul service mesh architectures by workload scope, traffic policy, identity, observability, operations, and ML fit.
KubernetesMLOpsObservability
Helm charts for ML platforms
Design, review, release, and operate Helm charts for ML platforms with predictable values, secure templates, upgrades, and ownership.
KubernetesGuides
Container image scanning for ML workloads
Build container image scanning into ML delivery with digest pinning, SBOMs, provenance, policy, remediation ownership, and runtime controls.
DockerKubernetesMLOps
OpenTelemetry for Java ML services
Instrument Java and Spring ML services with the OpenTelemetry agent, SDK, OTLP, domain spans, stable resource identity, and safe rollout controls.
ObservabilityMonitoringMLOps
SRE vs. DevOps for ML platforms
Clarify how DevOps and site reliability engineering complement each other through delivery, service objectives, error budgets, automation, and ML operations.
DevOpsMLOpsGuides
What is OpenTelemetry?
Understand OpenTelemetry signals, APIs, SDKs, semantic conventions, OTLP, the Collector, and how they fit into an ML observability architecture.
ObservabilityMonitoringMLOps
Kubernetes service mesh for ML platforms
Decide whether a service mesh fits your ML platform, then design traffic policy, identity, observability, rollout, and failure behavior deliberately.
KubernetesMLOpsObservability
Troubleshoot Kubernetes ImagePullBackOff errors
Diagnose ImagePullBackOff through Pod events, immutable image references, registry credentials, node networking, rate limits, and platform configuration.
KubernetesDockerGuides
Use kubectl exec safely for ML workload debugging
Use kubectl exec and ephemeral containers for targeted Kubernetes debugging without losing evidence, mutating workloads, or bypassing platform controls.
KubernetesGuides
Run Polyaxon AI workloads from GitHub Actions
Use GitHub Actions to validate and submit traceable AI, ML, and agent workloads to Polyaxon without turning CI runners into training infrastructure.
GitHubMLOpsAI Agents
Troubleshoot Kubernetes nodes in NotReady state
Diagnose Kubernetes NotReady nodes through conditions, heartbeats, kubelet, runtime, networking, pressure, cloud health, and workload impact.
KubernetesMonitoringGuides