What is AI observability?
AI observability connects traces, metrics, evaluations, feedback, and runtime context so teams can understand and improve models, applications, and agents.
MLOpsMonitoring
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Practical guides to building, running, and improving ML and AI in production.
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Distinguish sandboxes, runtimes, and code interpreters
Distinguish the security boundary, process runtime, and interpreter session when designing Polyaxon code execution for AI agents.
SandboxesArchitectureAI Agents
How to leverage Kubernetes metrics
Use Kubernetes resource, object-state, and control plane metrics to diagnose scheduling delays, size workloads, and design actionable alerts.
KubernetesGuides
Serve DeepSeek V4 on Polyaxon
Configure a DeepSeek V4 Pro service on B200 GPUs and check model loading, reasoning output, tool calls, and resource use.
LLMOpsInference
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.
KubernetesMLOpsGuides
Persist AI agent memory beyond the sandbox
Persist agent memory outside Polyaxon sandbox lifetimes using application stores, scoped connections, versioned snapshots, and reproducible evaluation.
AI AgentsMemoryStorage
Observe coding agents from prompt to sandbox execution
Use Polyaxon tracking, sandbox execution receipts, artifacts, and run comparison to investigate coding-agent failures and qualify changes.
AI AgentsObservabilityLLMOps
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.
KubernetesPlatform EngineeringMLOps
Use kubectl expose safely
Create and verify Kubernetes Services with kubectl expose while keeping selectors, ports, exposure scope, and production configuration explicit.
KubernetesGuides
Secure AI-generated code from proposal to execution
Qualify AI-generated code with Polyaxon sandbox controls, tracked execution receipts, reusable evaluation jobs, artifacts, and explicit promotion boundaries.
AI AgentsSecurityDevOps
Troubleshoot Kubernetes disk pressure
Diagnose Kubernetes DiskPressure, understand eviction signals, and prevent images, logs, and local ML data from exhausting node storage.
KubernetesMonitoringGuides
Manage long-lived AI agent workspaces
Manage long-lived Polyaxon agent workspaces with explicit ownership, reproducible environments, checkpoints, bounded lifetimes, and safe replacement.
SandboxesAI AgentsOperations
Build an AI agent governance workflow in Polyaxon
Use Polyaxon component versions, evaluation runs, pipeline dependencies, manual approval, and scoped execution to make agent releases reviewable.
AI AgentsGovernanceLLMOps
Schedule agent evaluations and backfill missed days
Run recurring agent evaluations and controlled historical backfills with Polyaxon schedules, date-range matrices, explicit window identity, and fresh results.
AI AgentsEvaluationAutomation
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.
KubernetesMLOpsGuides
Design data-residency controls for AI execution
Map AI execution data paths to Polyaxon compute placement, connections, artifacts, external providers, and documented residency controls.
SecurityData GovernanceInfrastructure
Build an LLM pipeline to classify ML failure reports
Use Polyaxon DAGs, tracked outputs, artifacts, and run comparison to develop a reviewable classifier for ML workload failure reports.
LLMOpsDataOpsEvaluation
Check Kubernetes version compatibility
Check kubectl, control-plane, node, and API compatibility before upgrades or while diagnosing inconsistent Kubernetes behavior.
KubernetesGuides
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.
KubernetesNetworkingGuides
Qualify AI-generated code before pipeline release
Qualify AI-generated code for Polyaxon pipelines using immutable candidate identity, independent evaluation, explicit approval, and versioned release artifacts.
AI AgentsCI/CDSecurity
Django logging on Kubernetes
Configure Django logs for container collection, useful request context, exception diagnosis, privacy, and correlation with Kubernetes workload state.
PythonKubernetesMonitoring
Isolate reinforcement-learning agent rollouts
Use Polyaxon to run reinforcement-learning agent rollouts with bounded execution, separate reward evaluation, reproducible seeds, and isolated trajectory artifacts.
Reinforcement LearningAI AgentsSecurity
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.
DockerMLOpsGuides
Build and evaluate LLM function calling
Connect bounded LLM tools to Polyaxon sandboxes, validate calls in the host, retain execution receipts, and compare tool behavior in repeatable evaluation runs.
AI AgentsToolsEvaluation