Llmops articles

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.

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.

Run batch LLM evaluations on Kubernetes

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

An AI agent deployment checklist for the first production release

Build an agent release checklist around Polyaxon qualification jobs, versioned components, artifact reports, termination settings, and manual approval.

Production LLM systems: Where to invest after the prototype

Use Polyaxon run tracking, comparison dashboards, resource monitoring, and repeatable evaluation to decide what to improve after an LLM prototype.

Build a conversational assistant on Kubernetes

Design a production conversational assistant with grounded retrieval, controlled model access, durable conversation state, evaluation, security, and Kubernetes operations.

How to evaluate LLM routers for cost, quality, and latency

Test LLM routing policies with task-level quality, cost, latency, fallbacks, route stability, and model-aware production evidence.

What is an AI gateway?

An AI gateway centralizes model access, routing, resilience, policy, cost controls, and telemetry across production LLM applications and agents.

Prompt versioning for production AI systems

Treat prompts as versioned production artifacts with lineage, evaluations, promotion workflows, rollback, ownership, and runtime observability.

LLM cost monitoring: Measure cost per successful task

Connect tokens, model calls, retrieval, tools, retries, and infrastructure with quality and task outcomes to control production LLM costs.

Offline vs. online evaluation for generative AI

Use offline evaluation for reproducible release decisions and online evaluation for real production behavior, then connect both in one feedback loop.

LLM-as-a-judge: Design and validate model-based evaluators

Use LLMs as scalable evaluators without treating them as ground truth: design clear rubrics, calibrate against humans, and monitor bias and drift.

Extend your MLOps workflow to AI agent development

Package an agent evaluator as a Polyaxon component, track candidate revisions and task metrics, and compare changes using existing MLOps workflows.

What is LLMOps? From prototype to production

LLMOps applies repeatable development, evaluation, deployment, and observability practices to production LLM applications and AI agents.

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.

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.

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.