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

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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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

1. Introduction to Generative AI — Foundation models, transformer architecture, and the generative AI landscape.
2. Operationalizing LLMs on Azure — Azure OpenAI, Azure ML, and production LLM deployment patterns.
3. Advanced Data Engineering — Retrieval pipelines, vector stores, and the data layer that LLMs need.
4. GenAI and LLMs on AWS — Bedrock, SageMaker, and AWS-native LLM operations.
5. Databricks to Local LLMs — Lakehouse-hosted LLM training through local and edge inference.
6. 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

Related Specializations

• 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