Turn production traces into regression tests
Turn a failure cluster into a reviewed regression case, preserve the causal tool response, and record baseline and candidate checks with Polyaxon.
LLMOpsObservabilityEvaluation
LLMOps
Learn how to evaluate, deploy, observe, and control the cost of LLM applications, from initial experiments to production feedback.
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Essential guides, selected for this topic.
What is LLMOps? From prototype to production
LLMOps applies repeatable development, evaluation, deployment, and observability practices to production LLM applications and AI agents.
LLMOpsMLOps
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.
EvaluationLLMOps
Optimize LLM inference with repeatable benchmarks
Compare inference optimizations against a fixed workload, latency limits, and quality checks, with benchmark configurations and results tracked in Polyaxon.
LLMOpsInferenceGuides
Serve multiple LoRA adapters with vLLM on Polyaxon
Deploy two task-specific LoRA adapters over one Qwen base model, select them by name, and measure quality and shared GPU capacity with Polyaxon.
LLMOpsvLLMInference
Latest articles
The latest guides, tutorials, and product updates.
Run batch inference with vLLM on Polyaxon
Generate predictions from a JSONL prompt file with vLLM, schedule the work on a Polyaxon GPU agent, and save the results as run artifacts.
LLMOpsvLLMInference
Serve multiple LoRA adapters with vLLM on Polyaxon
Deploy two task-specific LoRA adapters over one Qwen base model, select them by name, and measure quality and shared GPU capacity with Polyaxon.
LLMOpsvLLMInference
Train a decision classifier on Polyaxon
Fine-tune an open Qwen model for bounded support-routing decisions with Polyaxon jobs, track the adapter, and serve the reviewed version through vLLM.
LLMOpsFine TuningClassification
Deploy Laya typed decisions on Polyaxon
Serve the open Laya typed-decisions checkpoint through Polyaxon, with a CPU classifier endpoint, explicit model scope, and a review path for uncertain classifications.
LLMOpsInferenceClassification
Serve a Jev-style Qwen classifier on Polyaxon
Deploy an open Qwen model behind a typed classification API on Polyaxon, then check the decision schema and calibration before routing real work.
LLMOpsInferenceClassification
Serve DiffusionGemma on Polyaxon
Adapt Google's Gemma-on-Kubernetes deployment choices to Polyaxon with a DiffusionGemma vLLM service, a batch evaluation job, and an explicit compatibility checklist.
LLMOpsInferenceKubernetes
Separate evaluator changes from application improvements
Compare old and new application outputs under both evaluator versions, inspect changed decisions, and retain the four comparisons in Polyaxon.
LLMOpsEvaluationExperimentation
Make vLLM prefix caching work for repeated prompts
Understand exact prefix reuse in vLLM, structure repeated context, account for replica routing, and compare cold and warm workloads with Polyaxon.
LLMOpsInferencevLLM
Version the dataset behind every evaluation
Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.
DataOpsMLOpsLLMOps
Prevent data leakage in ML and LLM evaluation datasets
Choose evaluation boundaries, keep related examples together, audit duplicate overlap, and retain reproducible split manifests for ML and LLM experiments.
DataOpsMLOpsLLMOps
Optimize LLM inference with repeatable benchmarks
Compare inference optimizations against a fixed workload, latency limits, and quality checks, with benchmark configurations and results tracked in Polyaxon.
LLMOpsInferenceGuides
Tune LLM hyperparameters within a compute budget
Design a small LoRA fine-tuning sweep, control its execution in Polyaxon, and compare validation quality, resource use, and repeatability.
Hyperparameter TuningLLMOpsGuides
Connect LLM production traces to evaluation runs
Keep release, dataset, evaluator, and sampling context connected as you investigate LLM failures and compare fixes with Polyaxon.
LLMOpsObservabilityEvaluation
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.
LLMOpsKubernetesGuides
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.
LLMOpsGovernanceFinops
Run batch LLM evaluations on Kubernetes
Design reliable batch LLM evaluations with stable shards, bounded concurrency, GPU-aware placement, resumable results, and complete aggregation.
EvaluationKubernetesLLMOps
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.
AI AgentsLLMOpsGuides
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.
LLMOpsMLOpsGuides
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.
LLMOpsKubernetesAI Agents
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.
LLMOpsEvaluationObservability
What is an AI gateway?
An AI gateway centralizes model access, routing, resilience, policy, cost controls, and telemetry across production LLM applications and agents.
LLMOpsObservability
Prompt versioning for production AI systems
Treat prompts as versioned production artifacts with lineage, evaluations, promotion workflows, rollback, ownership, and runtime observability.
LLMOpsGuides
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.
LLMOpsMonitoring
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.
EvaluationLLMOps