Open Source LLMOps and Data Engineering Fundamentals

Modern AI applications require robust, scalable data pipelines to process massive datasets and feed Large Language Models efficiently. Managing these workflows requires a solid grasp of open-source data engineering and LLMOps principles. This text-based course guides you from foundational data concepts to orchestrating complex workflows. You will learn how to design, scale, and maintain the data systems that power LLM applications using industry-standard open-source tools. What you'll learn: Learn the core concepts of LLMOps, data engineering, and workflow orchestration; Configure and manage distributed tasks using Celery and Airflow; Structure complex data using graph databases and modern vector databases for LLM integration; Build resilient, automated data pipelines to ingest and process large-scale datasets; Apply Retrieval-Augmented Generation (RAG) patterns to connect your pipelines with AI models; Optimize pipeline performance and monitor system health using modern observability practices. You will start with key terminology and foundational definitions before exploring step-by-step written guides on orchestration, distributed task queues, and database integration. You will reinforce your knowledge through practical written exercises and conceptual code scenarios. This course is designed for beginners, software developers, and aspiring data professionals with no prior LLMOps experience. Start building reliable data systems for modern AI today.