LLMOps Guides for Production AI

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.

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.

Version the dataset behind every evaluation

Use Polyaxon data references, artifact logging, and registered versions to retain the dataset and split behind each evaluation.

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.

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.

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.

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.

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.

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.

What is LLMOps? From prototype to production

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

Serve DeepSeek V4 on Polyaxon

Configure a DeepSeek V4 Pro service on B200 GPUs and check model loading, reasoning output, tool calls, and resource use.

Observe coding agents from prompt to sandbox execution

Use Polyaxon tracking, sandbox execution receipts, artifacts, and run comparison to investigate coding-agent failures and qualify changes.