Set up a Polyaxon code execution workspace
Enable sandbox execution on a Polyaxon service, reuse its image and compute settings, run commands through the SDK, and preserve results before cleanup.
SandboxesPythonTutorials
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Practical guides to building, running, and improving ML and AI in production.
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How to evaluate LLM guardrails
Compare LLM guardrails using attack blocking, legitimate task success, false refusals, latency, cost, and explicit handling of errors.
GuardrailsRed TeamingEvaluation
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
Run ML workloads on your existing Kubernetes cluster
Plan a Polyaxon deployment on an existing Kubernetes cluster, covering GPUs, storage, identity, networking, workload ownership, and recovery.
KubernetesInfrastructureGuides
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
Run a sandbox workflow from the Polyaxon CLI
Start a sandbox-enabled Polyaxon service, upload two files, run a command, download its result, and release the workspace from the CLI.
ProductCliSandboxes
GPU cluster scheduling tools compared
Compare Kueue, KAI Scheduler, Volcano, Coscheduling, and Slurm Bridge by responsibility, workload fit, and Polyaxon integration path.
SchedulingKubernetes
AI red teaming metrics: measuring failures and coverage
Measure AI red teaming with explicit attack success rates, attempt budgets, coverage, false refusals, severity, and evaluator uncertainty.
Red TeamingEvaluationMetrics
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
Build a conversational assistant on Kubernetes
Design a production conversational assistant with grounded retrieval, controlled model access, durable conversation state, evaluation, security, and Kubernetes operations.
LLMOpsKubernetesAI Agents
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
Self-hosted vs. managed AI inference
Choose between self-hosted and managed AI inference using data policy, model access, latency, utilization, reliability, staffing, and cost per accepted outcome.
InferenceInfrastructureFinops
Contain generated code with Kubernetes runtime controls
Reduce the impact of generated-code failures with Polyaxon workload configuration, Kubernetes admission and network controls, minimal credentials, and incident evidence.
KubernetesSecurityAI Agents
How to improve GPU utilization
A practical guide to improving GPU utilization by diagnosing queue delays, input bottlenecks, resource fragmentation, sharing, and recovery overhead.
GuidesScheduling
Red teaming RAG systems
Test RAG systems for poisoned documents, cross-tenant retrieval, stale permissions, citation leaks, and unauthorized tool actions.
Red TeamingRAGEvaluation
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
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
How to red team AI agents
Test AI agent permissions, memory, handoffs, retries, and tool actions with a practical security matrix and reproducible evaluation workflow.
Red TeamingAI AgentsSecurity
Prompt versioning for production AI systems
Treat prompts as versioned production artifacts with lineage, evaluations, promotion workflows, rollback, ownership, and runtime observability.
LLMOpsGuides
From notebooks to repeatable ML jobs
Move notebook experiments into repeatable ML jobs with explicit inputs, versioned code, reproducible containers, and Polyaxon tracking.
MLOpsGuides
Review the trust boundary for LLM-generated code
Review the trust boundary for LLM-generated code before execution, mapping permissions, trusted controllers, output handling, and promotion controls in Polyaxon.
SecurityAI AgentsCode Execution
Scale agentic AI without breaking the infrastructure
Scale AI agents with admission control, dependency-aware concurrency, durable state, bounded authority, backpressure, and outcome-based capacity planning.
AI AgentsInfrastructureReliability
What is GPU orchestration?
Understand how GPU orchestration connects workflows, queues, resource placement, and recovery across shared ML infrastructure.
SchedulingOrchestration
How to test prompt injection in LLM applications
Build prompt injection tests for user input, retrieved documents, and tool responses, with checks for data access and actual side effects.
Red TeamingEvaluationSecurity