Module 12 - LLMOps Pipelines
Traditional MLOps was hard. LLMOps is harder in different ways.
In traditional ML, you version datasets and model weights, run training jobs, evaluate on holdout sets, and deploy model binaries. In LLM systems, prompts are code. Models change without warning. Evaluation requires another model to judge the output. Costs can explode overnight. Adapter weights proliferate across hundreds of customers. The primitives are different, and so are the failure modes.
This module covers the operational discipline required to run LLM systems in production - from treating prompts with the same rigor as software code to monitoring API costs before they become budget crises.
What You Will Learn
Lessons in This Module
| # | Lesson | Core Problem |
|---|---|---|
| 01 | Fine-Tuning Ops | 50 customers each needing a custom LLM adapter - serving them efficiently |
| 02 | Prompt Management | Unversioned prompt edit breaks customer chatbot for 2 hours |
| 03 | Evaluation Pipelines | Weekly model updates - automated evals to catch quality regressions |
| 04 | RAG Pipeline Ops | Stale index serving outdated knowledge to production users |
| 05 | Token Cost Monitoring | $40K in API costs in one week - monitoring to catch this in 24 hours |
LLMOps vs MLOps: The Key Differences
Prompts are code: A prompt change can improve or destroy output quality with no change to model weights. Prompts need version control, testing, peer review, and staged rollout - the same discipline as application code.
Non-determinism is the default: The same prompt at temperature > 0 produces different outputs every time. Traditional ML evaluation (compare model output to ground truth) breaks down. You need evaluation methods designed for stochastic outputs.
The model is an external dependency: In traditional ML, you own the model weights. In LLM systems using API providers, the model is a third-party service. The provider can change the model behavior, deprecate a version, or change pricing without warning.
Costs are a first-class metric: A model serving infrastructure cost is roughly fixed per request. LLM API costs scale with input + output token counts, which can vary by 100x between users and by 10x between prompt versions. Cost monitoring is an operational necessity, not an afterthought.
Evaluation requires another model: Checking whether a recommendation system improved click rates is straightforward. Checking whether an LLM's summarization quality improved requires either human evaluation (slow, expensive) or another LLM acting as a judge (fast, but introduces its own biases).
The LLMOps Stack
| Layer | Traditional MLOps | LLMOps |
|---|---|---|
| Code | Application code | Application code + Prompts |
| Versioning | DVC, Git-LFS for weights | Git for prompts, Prompt Registry |
| Evaluation | Offline metrics (AUC, RMSE) | LLM-as-judge, RAGAS, human eval |
| Serving | Model server (Triton, TorchServe) | LLM Gateway (rate limits, routing, caching) |
| Monitoring | Prediction drift, data drift | Cost/token, latency, refusal rate |
| Fine-tuning | Full model training | PEFT (LoRA), adapter registry |
| Retrieval | Feature store | Vector DB, RAG pipeline |
Prerequisites
- Module 10 - Monitoring and Observability
- Basic familiarity with LLMs (transformer architecture, prompt engineering concepts)
- Python (OpenAI SDK, LangChain basics)
- Module 11 - A/B Testing and Experimentation (for prompt A/B testing)