LLMOps with Kubernetes: Production GenAI Systems

Categories: MLOps
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About Course

Build, Deploy, and Operate Production-Grade GenAI Systems on Kubernetes with Autoscaling, GitOps, and Quality Gates

What Will You Learn?

  • Build a retrieval-augmented generation (RAG) system that pulls context from structured data and injects it into LLM prompts for fact-grounded responses
  • Fine-tune a large language model on CPU using parameter-efficient methods (LoRA) that reduce memory and time without sacrificing quality.
  • Deploy language models as Kubernetes services using vLLM for high-throughput inference with OpenAI-compatible APIs.
  • Design multi-tool agent workflows that decompose complex tasks (diagnose medical cases, book appointments, retrieve knowledge) into function calls.
  • Implement Kubernetes Agent Sandbox for isolated, sandboxed execution of untrusted agent code with strict RBAC and network policies.
  • Set up autoscaling infrastructure using KEDA for queue-depth-driven scaling and HPA for CPU-based scaling with custom metrics from Prometheus.
  • Adopt GitOps practices using ArgoCD to manage infrastructure as code, automate deployments, and enable predictable, auditable changes.
  • Orchestrate complex ML pipelines using Argo Workflows with conditional gates (e.g., run commit only if eval passes).
  • Integrate automated quality gates using DeepEval to measure RAG faithfulness and block deployments when quality thresholds aren't met
  • Add safety constraints to AI systems with input blocklists and output guardrails that prevent hallucinations and out-of-scope responses.
  • Observe end-to-end request flows using OpenTelemetry, distributed tracing (Tempo), and log aggregation (Loki) to debug production issues.
  • Design governance and audit trails for AI systems, including cost tracking, tool usage logs, and regulatory compliance evidence.

Course Content

Introduction to LLMOps

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