Query runs and model versions across projects
Use Polyaxon's organization and team APIs to inspect GPU workloads by agent, count model versions, and calculate job failure rates across projects.
PolyaxonPythonMonitoring
Latest articles
The latest guides, tutorials, and product updates.
Design reliable self-hosted ML infrastructure
Build self-hosted ML infrastructure around explicit failure domains, durable queues and artifacts, eligible failover, actionable telemetry, and tested recovery.
InfrastructureKubernetesMonitoring
Autoscale inference services using workload metrics
Use custom metrics with Kubernetes HPA for independent inference replicas, account for model startup, and distinguish desired replicas from usable capacity.
KubernetesInferenceMonitoring
SLOs for AI applications and agents
Define service-level objectives for AI quality, task success, safety, latency, availability, and cost using measurable user-centered indicators.
ObservabilityMonitoring
GPU utilization metrics: allocation, activity, and throughput
Learn which GPU utilization metrics explain capacity, device activity, memory pressure, and useful ML throughput—and how to avoid misleading averages.
MonitoringScheduling
LLM cost monitoring: Measure cost per successful task
Connect tokens, model calls, retrieval, tools, retries, and infrastructure with quality and task outcomes to control production LLM costs.
LLMOpsMonitoring
Kubernetes probes for ML services
Configure startup, readiness, and liveness probes for model servers and interactive ML services without causing restart loops or hiding dependency failures.
KubernetesMonitoringGuides
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.
KubernetesMonitoringGuides
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.
KubernetesMonitoringGuides
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
Troubleshoot Kubernetes disk pressure
Diagnose Kubernetes DiskPressure, understand eviction signals, and prevent images, logs, and local ML data from exhausting node storage.
KubernetesMonitoringGuides
Django logging on Kubernetes
Configure Django logs for container collection, useful request context, exception diagnosis, privacy, and correlation with Kubernetes workload state.
PythonKubernetesMonitoring
Monitor Node.js services on Kubernetes
Monitor Node.js services with request outcomes, event-loop delay, memory, dependencies, Kubernetes state, and low-cardinality telemetry.
KubernetesMonitoringGuides
Python logging for ML workloads
Create useful Python logs for training and batch workloads with structured context, exception details, stdout collection, and controlled volume.
PythonMonitoringGuides
Observability for machine learning
ML observability connects logs, metrics, artifacts, infrastructure signals, and model behavior so teams can debug training and serving systems.
MLOpsMonitoring
Use kube-state-metrics for ML platforms
Monitor Kubernetes object state for ML workloads while separating desired state, resource usage, application outcomes, and high-cardinality metadata.
KubernetesMonitoringGuides
Kubernetes audit logs for ML platforms
Design Kubernetes audit policy, collection, retention, and investigation workflows for shared ML clusters without recording sensitive payloads by default.
KubernetesMonitoringGuides
Monitor Django applications on Kubernetes
Connect Django request outcomes, database and cache health, worker behavior, Kubernetes state, and release context in one monitoring strategy.
PythonKubernetesMonitoring
Container logging for ML workloads
Design container logs for local Docker debugging and Kubernetes collection without losing run context, exhausting nodes, or exposing sensitive data.
DockerKubernetesMonitoring
OpenTelemetry Collector for ML platforms
Design OpenTelemetry Collector pipelines for ML services with clear receivers, processors, exporters, deployment patterns, and failure controls.
ObservabilityMonitoringMLOps
Monitor Amazon EKS for ML workloads
Build layered Amazon EKS monitoring for control-plane activity, Kubernetes state, nodes, GPUs, applications, ML runs, and telemetry health.
KubernetesMonitoringMLOps
Prometheus Alertmanager for ML platforms
Design Prometheus alerts and Alertmanager routing for ML platforms without noisy pages, missing owners, or unsafe high-cardinality labels.
MonitoringKubernetesMLOps
Monitoring vs. observability for ML systems
Separate monitoring from observability, then combine metrics, logs, traces, run context, and model evaluation into an effective ML operating model.
MonitoringObservabilityMLOps
Managed Prometheus for ML platforms
Decide between managed and self-operated Prometheus using scale, availability, PromQL compatibility, data governance, cost, and operational ownership.
MonitoringKubernetesMLOps