LLMOps articles

Build an AI agent governance workflow in Polyaxon

Use Polyaxon component versions, evaluation runs, pipeline dependencies, manual approval, and scoped execution to make agent releases reviewable.

Build an LLM pipeline to classify ML failure reports

Use Polyaxon DAGs, tracked outputs, artifacts, and run comparison to develop a reviewable classifier for ML workload failure reports.

Compare serverless and dedicated LLM inference with Polyaxon

Use Polyaxon components, matrices, tracking, and run comparisons to evaluate serverless model APIs against dedicated inference services on quality, latency, and cost.

Serve Qwen 3.6 on Polyaxon

Deploy Qwen3.6-27B with SGLang, then check the model's API behavior, memory use, and response quality before sending it production traffic.

Evaluate zero-shot prompting for production tasks

Use Polyaxon runs, versioned components, grid searches, case artifacts, and comparison views to qualify zero-shot prompts for production ML workflows.

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.

Turn LLM experiments into a reusable engineering knowledge base

Use Polyaxon run queries, comparison dashboards, artifact reports, and lineage to preserve the evidence behind LLM engineering decisions.

Optimize LLM performance and cost with controlled experiments

Track usage and pricing assumptions in Polyaxon, compare cost against quality in run dashboards, and control resources and concurrency during LLM experiments.

LLM agents: architecture and production operations

Map LLM agent reasoning, tools, execution, evaluation, and release workflows to Polyaxon services, sandboxes, jobs, components, and DAGs.

An evidence-based LLMOps maturity assessment

Assess LLMOps practices using Polyaxon run evidence: versioned components, comparison reports, artifact lineage, scheduling presets, and release approvals.

Design search, retrieval, and recommendations with LLMs

Compare lexical, dense, and hybrid retrieval with a Polyaxon grid search, then inspect ranking quality, answer evidence, latency, and recommendation outcomes.

Build knowledge-grounded LLM applications

Build and evaluate versioned RAG indexes with Polyaxon operations, resource configuration, connections, tracked manifests, and case-level reports.