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
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Practical guides to building, running, and improving ML and AI in production.
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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
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
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
Compare serverless and dedicated LLM inference with Polyaxon
Use Polyaxon components, matrices, tracking, and run comparisons to evaluate serverless model APIs against dedicated inference services on quality, latency, and cost.
LLMOpsInferenceInfrastructure
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
Serve Qwen 3.6 on Polyaxon
Deploy Qwen3.6-27B with SGLang, then check the model's API behavior, memory use, and response quality before sending it production traffic.
LLMOpsInference
Operate remote code execution with Polyaxon
Operate remote code execution through Polyaxon with explicit session ownership, command receipts, artifact collection, and safe retry decisions.
SandboxesAI AgentsOperations
Manage the lifecycle of a Polyaxon sandbox service
Create a Polyaxon sandbox service, wait for readiness, track command execution, preserve outputs, configure timeouts and culling, and stop resources when work finishes.
AI AgentsOrchestrationSecurity
Queue management for machine learning workloads
Why queue management matters for shared ML infrastructure and how Polyaxon handles priorities, concurrency, and workload scheduling.
SchedulingGuides
Keep Polyaxon sandboxes ready with bounded lifetimes
Keep Polyaxon sandbox services ready for active sessions while controlling idle cost, absolute lifetime, state persistence, and cleanup.
SandboxesPerformanceOperations
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
Give coding agents controlled access to Polyaxon operations
Build a coding assistant around Polyaxon run queries, sandbox commands, component versions, and reviewable operations that preserve project context and execution evidence.
AI AgentsMLOpsAutomation
Choose execution patterns for AI agents with Polyaxon
Use Polyaxon jobs, sandbox-enabled services, schedules, and tracking to match agent tasks to their execution, persistence, and capacity requirements.
AI AgentsInfrastructureOrchestration
Use kubectl patch safely
Choose strategic merge, JSON merge, or JSON Patch deliberately, preview changes, and reconcile emergency Kubernetes patches with declarative configuration.
KubernetesGuides
Reduce AI agent startup latency with Polyaxon
Reduce Polyaxon agent startup latency by measuring queueing, image preparation, initialization, and readiness before tuning capacity or workspace reuse.
AI AgentsPerformanceScheduling
Build and evaluate AI agent guardrails in Polyaxon
Use Polyaxon to version guardrail experiments, compare case-level outcomes, constrain execution, and require evidence before promoting an agent release.
AI AgentsSecurityEvaluation
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.
KubernetesSchedulingGuides