Connect LLM production traces to evaluation runs
Keep release, dataset, evaluator, and sampling context connected as you investigate LLM failures and compare fixes with Polyaxon.
LLMOpsObservabilityEvaluation
Latest articles
The latest guides, tutorials, and product updates.
What are your ML jobs connecting to?
Trace image pulls, Git clones, S3 and GCS access, Hugging Face downloads, and artifact uploads across the lifecycle of Kubernetes jobs and sandboxes.
KubernetesObservabilityInfrastructure
Beyond agent traces: State, artifacts, and recovery
Connect agent traces to checkpoint state, versioned artifacts, action receipts, and evaluations so failures lead to controlled recovery and reproducible improvements.
AI AgentsObservabilityEvaluation
Design measurable AI agents before you build
Define agent outcomes, prohibited actions, events, evaluators, budgets, segments, and feedback loops before implementation begins.
AI AgentsEvaluationObservability
Secure AIOps automation for ML platforms
Use AIOps to investigate ML incidents and automate low-risk remediation without giving an AI system unrestricted production access.
AIOpsSecurityObservability
Designing a control plane for AI agents
Separate live agent requests from versioning, evaluation, policy, rollout, identity, evidence, and recovery across the application lifecycle.
AI AgentsOrchestrationObservability
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
Measure AI agent workflow bottlenecks
Measure Polyaxon agent workflow bottlenecks across planning, queueing, execution, transfers, and evaluation using critical-path timing and quality-aware comparisons.
AI AgentsPerformanceObservability
MCP observability: Monitor tools, resources, and context
Trace MCP discovery, sessions, resources, prompts, tool calls, permissions, errors, and agent outcomes across clients and servers.
AI AgentsObservability
How to evaluate LLM routers for cost, quality, and latency
Test LLM routing policies with task-level quality, cost, latency, fallbacks, route stability, and model-aware production evidence.
LLMOpsEvaluationObservability
What is an AI gateway?
An AI gateway centralizes model access, routing, resilience, policy, cost controls, and telemetry across production LLM applications and agents.
LLMOpsObservability
Turn production traces into regression tests
Turn a failure cluster into a reviewed regression case, preserve the causal tool response, and record baseline and candidate checks with Polyaxon.
LLMOpsObservabilityEvaluation
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.
AI AgentsObservability
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
Investigate runtime threats in Polyaxon agent workloads
Correlate Polyaxon run records with external runtime-security events to investigate agent workloads, preserve evidence, and coordinate containment.
AI AgentsSecurityObservability
OpenTelemetry Collector for ML platforms
Design OpenTelemetry Collector pipelines for ML services with clear receivers, processors, exporters, deployment patterns, and failure controls.
ObservabilityMonitoringMLOps
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
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
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
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
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
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
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