Guides articles

Kubernetes objects for ML platform teams

Understand Kubernetes objects, desired state, metadata, ownership, and reconciliation through the lifecycle of an ML workload.

Managed Prometheus for ML platforms

Decide between managed and self-operated Prometheus using scale, availability, PromQL compatibility, data governance, cost, and operational ownership.

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.

Debug Kubernetes ML workloads

Debug ML workloads by following Kubernetes admission, scheduling, Pod startup, containers, nodes, networking, storage, and application outcomes in order.

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.

Kubernetes deployments: a complete guide

Learn how Kubernetes Deployments manage pods and ReplicaSets, support rollout strategies, and keep applications available.

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.

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.

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.

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.

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.

Kubernetes learning projects for ML engineers

Learn Kubernetes through practical ML projects covering Pods, Jobs, storage, networking, scheduling, observability, security, and failure recovery.

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.

Helm charts for ML platforms

Design, review, release, and operate Helm charts for ML platforms with predictable values, secure templates, upgrades, and ownership.

Container image scanning for ML workloads

Build container image scanning into ML delivery with digest pinning, SBOMs, provenance, policy, remediation ownership, and runtime controls.

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.

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.

What is OpenTelemetry?

Understand OpenTelemetry signals, APIs, SDKs, semantic conventions, OTLP, the Collector, and how they fit into an ML observability architecture.

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.

Troubleshoot Kubernetes ImagePullBackOff errors

Diagnose ImagePullBackOff through Pod events, immutable image references, registry credentials, node networking, rate limits, and platform configuration.

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.

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.

Troubleshoot Kubernetes nodes in NotReady state

Diagnose Kubernetes NotReady nodes through conditions, heartbeats, kubelet, runtime, networking, pressure, cloud health, and workload impact.

Single vs. multi-cluster Kubernetes for ML platforms

Choose between one Kubernetes cluster and multiple clusters using isolation, failure domains, data locality, accelerator access, operations, and cost.