llmops-platform-engineering
LLMOps Platform Engineering teaches you to architect internal LLM platforms that balance rapid experimentation with production safety. You'll implement model promotion pipelines with automated quality and safety gates, canary validation, and rollback capabilities, plus set up A/B testing infrastructure and observability across Kubernetes and cloud inference.
LLMOps Platform Engineering helps you design production LLM platforms with safe model promotion and evaluation gates.
AI-generated summary based on this skill's SKILL.md
Install
BagelHole/DevOps-Security-Agent-Skills/llmops-platform-engineering · repository language: Shell
git clone https://github.com/BagelHole/DevOps-Security-Agent-Skills
cp -r DevOps-Security-Agent-Skills/devops/ai/llmops-platform-engineering ~/.claude/skills/llmops-platform-engineeringnpx skillfed install BagelHole/DevOps-Security-Agent-Skills/llmops-platform-engineeringFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do you build an LLMOps platform with CI/CD?
LLMOps Platform Engineering teaches you to architect internal LLM platforms that integrate CI/CD pipelines with automated evaluation gates and quality checks. You'll design model promotion workflows that validate safety, performance, and compliance before production deployment, ensuring rapid experimentation doesn't compromise reliability.
What's the LLM model promotion pipeline in Kubernetes?
LLMOps Platform Engineering covers building model promotion pipelines on Kubernetes that orchestrate safe model advancement through staged gates. You'll learn to set up inference serving with auto-scaling, implement canary rollouts for gradual validation, and establish rollback capabilities—balancing speed with production safety.
How do you set up evaluation gates for model deployment?
LLMOps Platform Engineering guides you through establishing governance workflows with safety evaluation gates that validate models before promotion. You'll configure quality thresholds, implement automated checks for hallucination detection and guardrails, and create compliance workflows that gate deployment decisions based on measurable criteria.
What governance and compliance workflows matter for LLM deployment?
LLMOps Platform Engineering emphasizes establishing governance frameworks that enforce compliance across model lifecycle stages. You'll implement audit trails, version control in model registries, multi-environment promotion strategies, and observability systems that track safety metrics and cost analytics throughout production LLM inference infrastructure.
How can you configure A/B testing and canary rollouts?
LLMOps Platform Engineering teaches infrastructure for A/B testing LLM models and canary deployments that validate new versions safely. You'll design testing frameworks that measure latency, SLOs, and user outcomes, implement gradual traffic shifting to detect issues early, and establish rollback procedures when quality thresholds aren't met.
What observability and monitoring do production LLM systems need?
LLMOps Platform Engineering covers observability for LLM production systems including latency benchmarking, cost analytics, safety metric tracking, and SLO monitoring. You'll implement dashboards that surface model performance, inference costs, and compliance signals across Kubernetes clusters, enabling teams to detect degradation and respond quickly.
SKILL.md
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LLMOps Platform Engineering
Design and operate an internal LLM platform that supports rapid experimentation without compromising reliability, cost, or compliance.
When to Use This Skill
- Building an internal platform for teams to deploy and manage LLM-powered features
- Designing CI/CD pipelines that include model evaluation gates
- Setting up A/B testing infrastructure for model versions
- Creating Kubernetes-based model serving infrastructure
- Establishing governance workflows for model promotion
Prerequisites
- Kubernetes cluster with GPU node pools (or cloud inference API access)
- Container registry (Harbor, ECR, GCR, or ACR)
- CI/CD system (GitHub Actions, GitLab CI, or Argo Workflows)
- Observability stack (Prometheus + Grafana + OpenTelemetry)
- Model registry (MLflow or custom metadata store)
Outcomes
- Standardized path from experiment to production
- Safe model rollout with
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