Blog

Unify hybrid AI platform operations

Operate AI workloads across clusters and clouds with central intent, local execution, placement policy, consistent identity, connected evidence, and failure-aware control.

Reduce the cost of self-hosted ML workloads

Reduce self-hosted ML costs with outcome-based accounting, right-sized resources, elastic capacity, interruption-ready workloads, local data paths, and deliberate retention.

Design multi-tenant AI agent infrastructure on Kubernetes

Separate identity, network access, data, queues, budgets, and evidence when multiple teams or customers run AI agents on shared Kubernetes infrastructure.

Sandbox AI agents on Kubernetes

Design isolated Kubernetes execution for AI agents with a threat model, stronger runtimes, scoped identity, controlled egress, bounded storage, and auditable cleanup.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Design measurable AI agents before you build

Define agent outcomes, prohibited actions, events, evaluators, budgets, segments, and feedback loops before implementation begins.

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.

Design production security for AI agents

Contain AI agent identity, tools, memory, network access, and delegated actions with enforceable production boundaries.

Multi-cluster GPU orchestration

Design multi-cluster GPU orchestration around workload eligibility, data locality, queue routing, recovery, and clear dispatch ownership.

Run batch LLM evaluations on Kubernetes

Design reliable batch LLM evaluations with stable shards, bounded concurrency, GPU-aware placement, resumable results, and complete aggregation.

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.

Operating long-running AI agents

Choose jobs, services, queues, and capacity controls for agent workloads using task duration, dependency limits, resume latency, and execution cost.

Secure enterprise AI from data to deployment

Apply security controls across data, training, evaluation, artifacts, deployment, and operation without slowing every AI workload equally.

Place ML workloads close to their data

Design region-aware ML execution around dataset, registry, model, cache, artifact, and service locality without confusing proximity with data residency.