Intermediate-Advanced|18 hours|30 lessons

LLM Operations for MLOps Engineers

A comprehensive course covering 30 essential LLM concepts through the lens of MLOps engineering. Every lesson teaches the concept, then shows you how to operationalize it at scale, with real interview scenarios and system design questions from FAANG+ companies.

Text-based, no videos

6 modules, 30 lessons

Lifetime access

What you'll learn

What an LLM actually is, end to end: tokens, embeddings, parameters, latent space, and how each maps to infrastructure

The full model lifecycle: pre-training, fine-tuning (including LoRA), alignment, and RLHF, with the costs and tradeoffs of each

Prompting and context engineering: system prompts, context windows, zero-shot, few-shot, and chain-of-thought, with production tradeoffs

Inference at scale: latency budgets, sampling, hallucination detection, grounding, and serving architectures that hit p99 SLOs

Production architectures: RAG (simple and at scale), workflows, agents, and multimodal serving

Safety and governance: benchmarks, guardrails, observability, cost engineering, security, and rollout strategies

How to answer LLM system design questions in FAANG-level interviews using a real platform-engineering frame

Curriculum

6 modules · 30 lessons

LLM Foundations: What You're Actually Running

Build a precise mental model of what an LLM is, end to end, before you serve it in production.

5 lessons

What Is an LLM, Really?35 minFREE Tokens: The Unit of Everything30 minFREE Tokenization: Building the Vocabulary30 minFREE Embeddings: Turning Text into Math35 minFREE Latent Space and Parameters35 minFREE

The Model Lifecycle: From Training to Production

Pre-training, fine-tuning, alignment, and RLHF: where models come from and what each stage costs.

5 lessons

Pre-Training: Building the Foundation35 min Base Models vs Instruct Models30 min Fine-Tuning: Adapting Models to Your Domain40 min Alignment: Making Models Helpful and Safe35 min RLHF: The Feedback Loop40 min

Prompting and Context Engineering

System prompts, context windows, and prompting strategies that survive contact with production traffic.

3 lessons

Prompts, System Prompts, and User Prompts30 min Context Window: Managing the Bottleneck35 min Zero-Shot, Few-Shot, and Chain-of-Thought30 min

Inference and Performance: Running Models at Scale

Latency budgets, sampling, hallucination, and grounding: serving LLMs the way production demands.

5 lessons

Inference: Serving Predictions at Scale40 min Latency: Every Millisecond Matters35 min Temperature and Sampling: Controlling Randomness30 min Hallucination: When Models Lie Confidently40 min Grounding: Connecting Models to Reality35 min

Production Architectures: Building Real Systems

RAG, workflows, agents, and multimodal systems: the patterns behind every serious LLM product.

5 lessons

RAG: Retrieval-Augmented Generation45 min RAG at Scale: When Simple RAG Breaks40 min Workflows: Orchestrating LLM Pipelines35 min Agents: When LLMs Make Decisions40 min Multimodality: Beyond Text35 min

Safety, Evaluation, and Governance

Benchmarks, guardrails, observability, cost, security, deployment, and the capstone end-to-end design.

7 lessons

Benchmarks: Measuring What Matters35 min Guardrails: Keeping Models Safe35 min LLM Observability: Monitoring What You Can't Unit Test40 min Cost Engineering: LLMs Are Expensive40 min LLM Security: Attack Surfaces and Defenses40 min Model Deployment Strategies35 min Capstone: Design an End-to-End LLM Platform60 min

About the Author

Sharon Sahadevan

AI Infrastructure Engineer

Building production GPU clusters on Kubernetes. H100s, large-scale model serving, and end-to-end ML infrastructure across Azure and AWS.

10+ years designing cloud-native platforms with deep expertise in Kubernetes orchestration, GitOps (Argo CD), Terraform, and MLOps pipelines for LLM deployment.

Author of KubeNatives, a weekly newsletter read by 3,000+ DevOps and ML engineers for production insights on K8s internals, GPU scheduling, and model-serving patterns.

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