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LLMOps Engineering

L4-L5 · 7 courses · 116 chapters

Monitor hallucination rates and token costs, operate guardrails and eval gates, manage prompt versioning and canary deployments.

What you'll learn

Core responsibilities this discipline prepares you for.

Design CI/CD pipelines

for LLM application deployment

Build ArgoCD GitOps workflows with Helm-based deployments and environment promotion
Implement canary and blue-green rollout strategies with automated quality-based rollback
Wire complete CI/CD pipelines that trigger rollbacks when evaluation metrics degrade

Monitor LLM systems in production

— latency, errors, costs, quality

Instrument with OpenTelemetry and Langfuse v3 for OTEL-native distributed tracing
Build Grafana dashboards with Logfire for Python application monitoring and alerting
Set up monitoring stacks that detect anomalies, fire alerts, and enable trace-based root cause analysis

Manage LLM gateway operations

— key rotation, failover, quota management

Operate LiteLLM gateway: API key lifecycle management, provider health monitoring, per-team quotas
Handle zero-downtime model version switching with traffic draining and validation
Simulate provider outages and quota exhaustion to validate failover and degradation behavior

Implement FinOps practices

— cost attribution, budgets, and optimization

Track token costs by team, feature, and model with Prometheus-based budget alerting
Implement cost optimization through semantic caching, model tiering, and prompt compression
Build FinOps dashboards that demonstrate measurable cost reduction across optimization strategies

Build continuous evaluation pipelines

for production LLM quality

Run RAGAS and DeepEval evaluation pipelines alongside production traffic as shadow evaluators
Set up Langfuse-based quality tracking with automated quality gates and threshold alerting
Detect quality degradation in real time and trigger automated alerts when scores drop below baselines

Detect and respond to prompt attacks

and safety incidents in production

Monitor NeMo Guardrails operationally for prompt injection and jailbreak detection patterns
Classify incident severity and execute structured response workflows with containment procedures
Simulate attack scenarios end-to-end: detection, triage, remediation, and post-incident analysis

Manage data quality for RAG systems

— freshness, drift, accuracy

Monitor embedding drift and retrieval accuracy with continuous RAGAS evaluation
Set up automated reindexing triggers and stale content detection pipelines
Build monitoring for live RAG systems that detects quality degradation and triggers reindexing workflows

Implement capacity planning

— predict demand and right-size deployments

Forecast token demand using historical usage patterns and run load tests for LLM services
Model SLA capacity requirements and configure KEDA-based autoscaling policies
Run load tests that predict capacity requirements and validate SLA compliance under variable traffic

Your learning path

7 courses · sequenced for compounding · 116 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate10 Ch

Step 4

DevOps Foundations for GenAI Engineers

CI/CD, GitOps, observability — the DevOps practices that make GenAI deployments reliable and reproducible.

Advanced11 Ch

Step 5

Enterprise LLM Customization

Customize LLMs for enterprise — prompt engineering, RAG at scale, fine-tuning, and domain adaptation techniques.

Advanced35 Ch

Step 6

GenAI Evaluation, Safety & Governance

Evaluate, red-team, and govern GenAI systems — offline evals, online metrics, safety guardrails, compliance.

Advanced10 Ch

Capstone

GenAI Operations

Run GenAI in production — monitoring, dunning, incident response, cost control, and the on-call runbook.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

Prometheus

Scrape LLM-app metrics in labs

Grafana

Dashboards for p95 latency + error rates

OpenTelemetry

Distributed tracing across LLM calls

ArgoCD

GitOps deploys for lab stacks

Helm

Package LLM-app charts for deploy labs

Langfuse

Trace and replay every LLM interaction

GitHub Actions

CI for lab deployments

Argo Workflows

Orchestrate eval and training labs

PagerDuty

Wire incident response in the on-call lab

LiteLLM

Gateway for multi-provider failover labs

DeepEval

Automated eval suites in labs

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