What is sovereign AI? Control across the AI lifecycle
Define sovereign AI as control over data, models, compute, operations, providers, and evidence, then implement it with Kubernetes.
InfrastructureKubernetesSecurity
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Practical guides to building, running, and improving ML and AI in production.
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Fine-tune Mistral 7B with LoRA on Kubernetes
Plan a Mistral 7B LoRA fine-tuning workflow on Kubernetes with versioned data, GPU scheduling, Polyaxon tracking, evaluation, and adapter packaging.
LLMOpsKubernetesGuides
Retry failed evaluation shards with Indexed Jobs
Give evaluation shards stable indexes and independent retry budgets with Kubernetes Indexed Jobs, while keeping incomplete results visible.
KubernetesEvaluationPipelines
Run Promptfoo evaluations on Kubernetes with Polyaxon
Package a Promptfoo suite as a Polyaxon job, export evaluation reports, track artifacts, and verify that failed checks fail the workload.
PromptfooEvaluationKubernetes
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
Pods, Jobs, or Services for AI agents?
Choose the Kubernetes execution shape for an AI agent by separating logical task identity from Pods, Jobs, Services, workflows, and sandbox lifecycles.
AI AgentsKubernetesGuides
Orchestrate parallel coding agents with Polyaxon
Run parallel coding agents in separate Polyaxon workspaces, collect candidate patches, evaluate independently, and merge only reviewed changes.
AI AgentsSandboxesCollaboration
Build an enterprise AI security operating model
Define ownership, risk tiers, platform boundaries, exceptions, and evidence so enterprise AI security operates continuously instead of as a launch checklist.
SecurityGovernanceMLOps
Govern model access with an AI gateway
Operate an AI gateway as a policy boundary for workload identity, eligible models, routing, budgets, privacy, evidence, and controlled configuration changes.
LLMOpsGovernanceFinops
Restore notebook workspaces from volume snapshots
Create a CSI snapshot of a notebook workspace, restore it into a separate PVC, and verify files and application state before switching workspaces.
KubernetesStorageNotebooks
Kubernetes for AI agents: A platform engineering guide
Design a Kubernetes platform for AI agents with clear workload boundaries, durable state, scoped access, resource controls, and end-to-end evidence.
AI AgentsKubernetesInfrastructure
Scan model artifacts before adding them to a registry
Add static model artifact scanning before registry promotion, preserve scan evidence, check coverage, and bind approval to immutable artifact digests.
Model RegistrySecurityMLOps
Hold Pods until they are ready for scheduling
Use Kubernetes scheduling gates to wait for external prerequisites before placement, with a controlled example and clear ownership of gate removal.
KubernetesSchedulingPipelines
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
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
Build an operating model for agentic AI
Operate agentic AI with explicit service ownership, platform contracts, task lifecycle rules, change control, incident response, SLOs, and financial accountability.
AI AgentsPlatform EngineeringOperations
Design production security for AI agents
Contain AI agent identity, tools, memory, network access, and delegated actions with enforceable production boundaries.
AI AgentsSecurityGovernance
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
Connect agent execution to the ML platform
Connect AI agent execution to Polyaxon projects, components, scheduling, tracking, and artifacts through a stable application-owned execution contract.
AI AgentsMLOpsArchitecture
Multi-cluster GPU orchestration
Design multi-cluster GPU orchestration around workload eligibility, data locality, queue routing, recovery, and clear dispatch ownership.
OrchestrationKubernetes
Run batch LLM evaluations on Kubernetes
Design reliable batch LLM evaluations with stable shards, bounded concurrency, GPU-aware placement, resumable results, and complete aggregation.
EvaluationKubernetesLLMOps
Build a shared library of agent evaluators
Package application-owned agent evaluators as versioned Polyaxon components with typed contracts, reusable execution profiles, and consistent result evidence.
AI AgentsEvaluationAutomation
ML infrastructure explained for business teams
Understand what ML infrastructure pays for, how it affects delivery and reliability, and how to evaluate an investment using measurable workflow outcomes.
MLOpsInfrastructureGuides