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
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Practical guides to building, running, and improving ML and AI in production.
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Operate dynamic agent plans with bounded execution
Operate dynamic AI agent plans with Polyaxon execution, explicit policy checks, cumulative budgets, durable request state, and independent evaluation.
AI AgentsOrchestrationGovernance
Use kubectl edit safely
Use kubectl edit for deliberate live Kubernetes changes while avoiding controller conflicts, configuration drift, wrong-cluster edits, and unrecoverable fixes.
KubernetesGuides
Kubernetes CPU limits and throttling overview
Understand Kubernetes CPU requests, limits, throttling, and the failure modes caused by weak resource configuration.
KubernetesGuides
Verified data pipelines for reliable AI agents
Design agent data paths that preserve source identity, versions, permissions, freshness, retrieval evidence, and verification results.
AI AgentsDataOpsEvaluation
Offline vs. online evaluation for generative AI
Use offline evaluation for reproducible release decisions and online evaluation for real production behavior, then connect both in one feedback loop.
EvaluationLLMOps
GPU sharing on Kubernetes: MIG vs. time-slicing
Compare NVIDIA MIG and GPU time-slicing on Kubernetes, including memory isolation, resource names, scheduling behavior, and workload fit.
GPUKubernetesInfrastructure
Manage Kubernetes contexts safely
Use explicit kubeconfig contexts, namespaces, identities, and verification checks to reduce wrong-cluster changes across development, staging, and production.
KubernetesGuides
Estimate the operating cost of AI sandboxes
Estimate AI sandbox operating costs from measured active and idle resources, retries, storage, and engineering ownership using Polyaxon workload evidence.
AI AgentsCost OptimizationInfrastructure
Design reliable ML pipeline orchestration
Design ML pipelines with explicit dependencies, resource placement, safe caching, bounded retries, and complete evaluation evidence in Polyaxon.
OrchestrationPipelinesGuides
Build evaluation leaderboards with Polyaxon joins
Use Polyaxon joins to select comparable evaluation runs and build application-owned leaderboards with explicit cohort, completeness, and artifact evidence.
AI AgentsEvaluationAutomation
LLM-as-a-judge: Design and validate model-based evaluators
Use LLMs as scalable evaluators without treating them as ground truth: design clear rubrics, calibrate against humans, and monitor bias and drift.
EvaluationLLMOps
What is an AI sandbox? Development environments and execution isolation
Understand AI sandboxes for development and agent execution, including runtime isolation, credentials, storage, network access, GPUs, and lifecycle.
SandboxesAI AgentsKubernetes
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
Manage AI coding-agent sandboxes with Polyaxon
Manage Polyaxon sandbox-enabled services from versioned environment templates through readiness checks, process execution, file persistence, and resource cleanup.
AI AgentsOrchestrationSecurity
Evaluate AI agent infrastructure reliability
Evaluate AI agent infrastructure reliability through task-level outcomes, recovery behavior, Polyaxon execution evidence, and explicit operational ownership.
AI AgentsReliabilityInfrastructure
Extend your MLOps workflow to AI agent development
Package an agent evaluator as a Polyaxon component, track candidate revisions and task metrics, and compare changes using existing MLOps workflows.
AI AgentsMLOpsLLMOps
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
GPU jobs stuck Pending on Kubernetes: a debugging guide
Diagnose Pending GPU jobs by checking queue admission, scheduler events, advertised GPU resources, placement constraints, storage, and node capacity.
GPUKubernetesTroubleshooting
Kubernetes RBAC for ML workloads
Design least-privilege Kubernetes access for ML workloads with clear subjects, namespaced roles, dedicated service accounts, permission checks, and reviewable policy.
KubernetesSecurityGuides
Train agents with Ray and RAGEN
Run RAGEN on a Polyaxon-managed Ray cluster, with matching training resources, persistent checkpoints, and reproducible environment configuration.
AI AgentsReinforcement LearningDistributed Training
Build an ML knowledge repository your team can reuse
Connect experiment records, dataset versions, model artifacts, and review decisions into a reusable ML knowledge repository with Polyaxon.
MLOpsModel RegistryGuides
How to evaluate RAG systems
Evaluate retrieval and generation separately and end to end with representative datasets, groundedness checks, retrieval metrics, and production feedback.
RAGEvaluation
What is AI red teaming?
Design scoped adversarial tests for AI applications, examine tool and retrieval boundaries, and turn findings into regression coverage.
Red TeamingEvaluationAI Agents