Large Language Model Operations (LLMOps) — Duke University Coursera Specialization

2026-04-17 · 4min · Pragmatic AI Labs

Table of Contents

Six courses covering LLMOps end-to-end — generative AI foundations, Azure LLM operations, advanced data engineering, AWS GenAI, Databricks-to-local deployment, and the open-source LLMOps stack. The Duke University curriculum for engineers shipping LLMs to production.

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Production LLM architecture patterns using Rust, AWS, and Bedrock.

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What You Will Build

A production LLMOps pipeline: fine-tuned and prompt-engineered LLMs on Azure and AWS, advanced data engineering for retrieval and training pipelines, Databricks-hosted LLM workloads, and open-source deployment patterns for local and edge inference.

Courses in This Specialization

Introduction to Generative AI — Foundation models, transformer architecture, and the generative AI landscape.
Operationalizing LLMs on Azure — Azure OpenAI, Azure ML, and production LLM deployment patterns.
Advanced Data Engineering — Retrieval pipelines, vector stores, and the data layer that LLMs need.
GenAI and LLMs on AWS — Bedrock, SageMaker, and AWS-native LLM operations.
Databricks to Local LLMs — Lakehouse-hosted LLM training through local and edge inference.
Open Source LLMOps Solutions — The open-source stack: vLLM, Ollama, llama.cpp, and friends.

Who This Is For

ML and MLOps engineers moving from classical ML to LLMs
Platform engineers building internal LLM platforms
Data engineers owning the retrieval and RAG layer

MLOps | Machine Learning Operations — MLOps foundations this extends
Enterprise AI and Data Engineering with Databricks — lakehouse-native LLMOps
AI Tooling — production AI tooling around LLMs

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