LLMOps with Kubernetes: Production GenAI Systems

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.