Blog

AI agent tracing: How to debug tools, loops, and handoffs

AI agent tracing connects model calls, retrieval, tools, state, and handoffs so teams can explain failures, latency, cost, and outcomes.

Load-test ML services on Kubernetes

Design repeatable Kubernetes load tests for model services using realistic arrivals, tail latency, queueing, accelerator metrics, and recovery criteria.

Build golden paths for enterprise AI delivery

Create self-service AI delivery paths with explicit workload contracts, reusable components, governed connections, evaluation gates, evidence, and safe exceptions.

How to evaluate AI agents

A practical framework for evaluating AI agent outcomes, trajectories, tool use, safety, latency, and cost before and after release.

Kubernetes startup probes for model servers

Give model servers enough time to load weights and warm runtimes without weakening liveness detection for the rest of their lifecycle.

What is LLMOps? From prototype to production

LLMOps applies repeatable development, evaluation, deployment, and observability practices to production LLM applications and AI agents.

Make Kubernetes workflows accessible

Design Kubernetes and ML platform workflows that remain usable with keyboards, assistive technology, low-vision settings, and different ways of working.

What is distributed learning?

Distributed learning splits model training across processors or machines. Learn the main strategies, tradeoffs, and how to run it on Kubernetes.

What is AI observability?

AI observability connects traces, metrics, evaluations, feedback, and runtime context so teams can understand and improve models, applications, and agents.

How to leverage Kubernetes metrics

Learn the Kubernetes metrics that matter for resource usage, cluster state, control plane health, and day-to-day workload debugging.

Microservices on Kubernetes for ML platforms

Choose service boundaries for Kubernetes-based ML platforms without turning every component, model, or workflow step into a separate microservice.

Make a Kubernetes platform ready for AI workloads

Assess and close the gaps in accelerator access, batch scheduling, inference, data, identity, observability, cost, and ownership before AI workloads scale on Kubernetes.

Use kubectl expose safely

Create and verify Kubernetes Services with kubectl expose while keeping selectors, ports, exposure scope, and production configuration explicit.

Troubleshoot Kubernetes disk pressure

Diagnose Kubernetes DiskPressure, understand eviction signals, and prevent images, logs, and local ML data from exhausting node storage.

When Kubernetes is the right platform for ML

Evaluate whether Kubernetes provides enough scheduling, isolation, portability, and operational leverage to justify its complexity for ML workloads.

Check Kubernetes version compatibility

Check kubectl, control-plane, node, and API compatibility before upgrades or while diagnosing inconsistent Kubernetes behavior.

Kubernetes networking for ML workloads

Understand how Pods, Services, DNS, ingress, egress, and network policy shape the data paths used by training jobs and model services.

Django logging on Kubernetes

Configure Django logs for container collection, useful request context, exception diagnosis, privacy, and correlation with Kubernetes workload state.

Lint ML Dockerfiles with Hadolint

Use Hadolint to catch Dockerfile problems early while keeping base-image policy, dependency pinning, security scanning, and runtime validation separate.

Monitor Node.js services on Kubernetes

Monitor Node.js services with request outcomes, event-loop delay, memory, dependencies, Kubernetes state, and low-cardinality telemetry.

Queue management for machine learning workloads

Why queue management matters for shared ML infrastructure and how Polyaxon handles priorities, concurrency, and workload scheduling.

Python logging for ML workloads

Create useful Python logs for training and batch workloads with structured context, exception details, stdout collection, and controlled volume.

Use kubectl patch safely

Choose strategic merge, JSON merge, or JSON Patch deliberately, preview changes, and reconcile emergency Kubernetes patches with declarative configuration.

Kubernetes taints and tolerations for ML workloads

Keep ordinary Pods away from specialized nodes and combine tolerations with positive placement rules for GPU and interruptible ML capacity.