Infrastructure articles

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.

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.

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.

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.

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.

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.

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.

Docker build caching for ML workloads on Kubernetes

Reduce container build and startup time for ML jobs with reusable dependency layers, persistent build caches, and deliberate image and model caching.

Design open infrastructure for portable AI workloads

Keep AI workloads portable with explicit execution contracts, open packaging and telemetry, hardware abstraction, data boundaries, and tested migration paths.

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.

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.

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.

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.

Prepare an AI platform for next-generation GPUs

Make AI platforms ready for new accelerator generations through portable workload contracts, device-aware scheduling, topology, storage, compatibility, and migration evidence.

Five shifts shaping enterprise AI platforms

Plan enterprise AI platforms around measurable outcomes, heterogeneous compute, durable agents, cost per successful task, and continuous governance.

Manage cloud infrastructure for ML platforms

Operate cloud infrastructure for ML with declarative provisioning, clear ownership, workload isolation, capacity policies, cost allocation, and recovery exercises.