Blog

Use kube-state-metrics for ML platforms

Monitor Kubernetes object state for ML workloads while separating desired state, resource usage, application outcomes, and high-cardinality metadata.

Separate agent planning, execution, and evaluation

Structure Polyaxon agent systems around separate planning, execution, and evaluation contracts with explicit state and promotion decisions.

Kubernetes audit logs for ML platforms

Design Kubernetes audit policy, collection, retention, and investigation workflows for shared ML clusters without recording sensitive payloads by default.

Investigate runtime threats in Polyaxon agent workloads

Correlate Polyaxon run records with external runtime-security events to investigate agent workloads, preserve evidence, and coordinate containment.

Monitor Django applications on Kubernetes

Connect Django request outcomes, database and cache health, worker behavior, Kubernetes state, and release context in one monitoring strategy.

Stop expensive agent evaluation sweeps early

Use Polyaxon failure and metric early-stopping rules to bound agent evaluation sweeps while preserving complete evidence and avoiding premature quality decisions.

Container logging for ML workloads

Design container logs for local Docker debugging and Kubernetes collection without losing run context, exhausting nodes, or exposing sensitive data.

Use MCP and function calling in Polyaxon agents

Choose direct function tools or MCP adapters for Polyaxon agents based on reuse, authorization, lifecycle, and execution contracts.

Kubernetes CronJobs for ML automation

Schedule repeatable Kubernetes jobs with explicit time zones, concurrency, deadlines, history limits, idempotency, and observable outcomes.

Secure the data lifecycle of production LLM applications

Configure Polyaxon workload credentials, service accounts, connections, and artifact handling while protecting LLM data across providers, caches, and evaluation.

Kubernetes nodes for ML platforms

Understand node components, conditions, capacity, labels, taints, failure behavior, and lifecycle management for Kubernetes ML clusters.

Remove the bottlenecks blocking AI platform delivery

Diagnose AI platform bottlenecks across ownership, integration, delivery, infrastructure, feedback, and skills, then improve the highest-leverage constraint first.

Connect MCP servers to Polyaxon workflows

Design application-owned MCP tools that submit and inspect Polyaxon workflows while preserving authorization, bounded inputs, and run-level evidence.

Kubernetes persistent volumes for ML workloads

Choose PersistentVolumes, claims, StorageClasses, access modes, and reclaim policies for ML workspaces, caches, checkpoints, and services.

Choose a managed Kubernetes service for ML

Evaluate managed Kubernetes services for ML using responsibility, GPUs, networking, storage, identity, observability, cost, and portability.

Define trusted execution boundaries for AI workloads

Distinguish confidential computing from workload isolation, and define how Polyaxon scheduling and evidence fit a trusted execution design.

OpenTelemetry Collector for ML platforms

Design OpenTelemetry Collector pipelines for ML services with clear receivers, processors, exporters, deployment patterns, and failure controls.

Build an AI agent infrastructure stack with Polyaxon

Map an AI agent stack to Polyaxon services, jobs, sandboxes, queues, connections, tracking, and versioned components without conflating their responsibilities.

Monitor Amazon EKS for ML workloads

Build layered Amazon EKS monitoring for control-plane activity, Kubernetes state, nodes, GPUs, applications, ML runs, and telemetry health.

Tune RAG retrieval with Polyaxon experiment matrices

Compare RAG chunk sizes, retrieval depth, and model choices with Polyaxon experiment matrices, fixed evaluation inputs, and quality-aware run comparisons.

Kubernetes Pods for ML workloads

Learn what Pods provide, what they do not preserve, and how to design resources, lifecycle, sidecars, storage, and debugging for ML workloads.

Deploy AI agents as Polyaxon services

Package and deploy an AI agent as a Polyaxon service with explicit ports, health checks, scoped connections, and release evidence.

Kubernetes ReplicaSets explained

Understand how ReplicaSets maintain Pod replicas, why Deployments normally own them, and where they fit in ML workloads.

Connect agent tool calls to Polyaxon execution

Translate agent tool requests into authorized Polyaxon sandbox execution with bounded commands, structured results, and explicit lifecycle ownership.