Overview
The LLMOps interview for production AI systems, answered. Nine volumes cover the foundations of LLMOps, development and experimentation, deployment and infrastructure, monitoring, evaluation and observability, optimization and cost engineering, production systems and real-world scenarios, CI/CD and release engineering, AgentOps, guardrails and governance, and LLM data, fine-tuning operations and FinOps. Every volume ends with hands-on questions that hand you a config, trace or log, and scenario questions. Many answers carry a diagram.
What you will learn
- Explain the LLM lifecycle and what LLMOps adds to MLOps
- Run prompt, model and RAG experiments with proper tracking
- Deploy and scale LLM inference infrastructure
- Monitor, evaluate and observe LLM systems in production
- Cut latency and cost without losing quality
- Ship LLM changes safely with CI/CD and release controls
- Operate agents with guardrails and governance
- Manage LLM data, fine-tuning operations and spend
Included with the kit
- 63 written parts, yours for good
- 12h 23m of reading, measured not estimated
- Written for the advanced level
- Every future revision included
- UPI, cards and netbanking
Prerequisites
- Experience deploying software or ML systems
- Working knowledge of LLM applications
Curriculum
9 sections · 63 parts · 12h 23m01Volume 1: Foundations of LLMOps7 parts · 80 min
Understand how LLM systems differ from traditional ML systems
- What is LLMOps19 min
- LLM System Components8 min
- Types of LLM Applications8 min
- Key Challenges in LLMOps10 min
- LLM Lifecycle15 min
- LLMOps foundations: hands-on9 min
- LLMOps foundations: scenarios11 min
02Volume 2: Development & Experimentation7 parts · 95 min
How to build and iterate LLM systems
- Prompt Engineering at Scale17 min
- Model Selection34 min
- RAG Integration (LLM's Role)9 min
- Evaluation During Development9 min
- Experiment Tracking8 min
- Development: hands-on8 min
- Development: scenarios10 min
03Volume 3: Deployment & Infrastructure8 parts · 75 min
Ship LLM systems to production
- Deployment Patterns9 min
- Serving Architecture8 min
- Scaling LLM Systems8 min
- Deployment: Latency Optimization11 min
- Self-Hosted Model Serving8 min
- Security Basics12 min
- Deployment: hands-on8 min
- Deployment: scenarios11 min
04Volume 4: Monitoring, Evaluation & Observability7 parts · 74 min
Understand if your system is working correctly
- LLM Evaluation (Core)16 min
- RAG Evaluation8 min
- Observability10 min
- Online Monitoring7 min
- Debugging Failures13 min
- Monitoring and evaluation: hands-on9 min
- Monitoring and evaluation: scenarios11 min
05Volume 5: Optimization & Cost Engineering8 parts · 81 min
Make systems efficient and scalable
- Cost Optimization14 min
- Quantization14 min
- Model Distillation15 min
- Optimization and cost: Latency Optimization10 min
- Prompt Optimization5 min
- RAG Optimization5 min
- Optimization and cost: hands-on8 min
- Optimization and cost: scenarios10 min
06Volume 6: Production Systems & Real-World Scenarios8 parts · 78 min
Apply everything in real-world setups
- Production Chatbot Systems10 min
- Enterprise RAG Systems9 min
- Agent-Based Systems12 min
- Multi-Model Systems8 min
- Continuous Improvement Loop9 min
- Benchmarks & Model Evaluation in Practice10 min
- Production scenarios: hands-on9 min
- Production scenarios: scenarios11 min
07Volume 7: CI/CD, Release Engineering & Reliability6 parts · 85 min
Test whether a candidate can ship prompt, model, and index changes safely through eval-gated pipelines, staged rollouts, SLOs, and tested recovery plans, and keep the system reliable on day 2.
- Eval-Gated CI/CD for LLM Apps16 min
- Release Management for Prompts, Models & Indexes14 min
- SLOs, Load Testing & Capacity Operations14 min
- Resilience & Disaster Recovery12 min
- CI/CD and reliability: hands-on14 min
- CI/CD and reliability: scenarios15 min
08Volume 8: AgentOps, Guardrails & Governance6 parts · 87 min
Test whether a candidate can operate agents, tools, guardrails, and AI governance processes safely in production, from trajectory evals and MCP server ops to red-teaming, audit trails, and regulatory readiness.
- AgentOps: Evaluation & Operations17 min
- Tool & MCP Server Operations12 min
- Guardrails & Safety Operations14 min
- Governance, Compliance & Audit14 min
- AgentOps and governance: hands-on14 min
- AgentOps and governance: scenarios16 min
09Volume 9: LLM Data, Fine-Tuning Ops & FinOps6 parts · 88 min
Operate the data, fine-tuning, provider API, and spend side of production LLM systems so that datasets stay clean and traceable, adapters ship safely, and every dollar of token spend is attributed, forecast, and controlled.
- LLM Data Operations17 min
- Fine-Tuning & Adapter Lifecycle Operations14 min
- Provider API Operations12 min
- LLM FinOps & Unit Economics15 min
- Data and FinOps: hands-on14 min
- Data and FinOps: scenarios16 min
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