Data Engineering Fundamentals for MLOps

Machine learning models are only as good as the data that feeds them, yet transitioning raw data into production-ready pipelines remains a major challenge. This course helps you bridge the gap between data engineering and machine learning operations.\n\nYou will gain a solid foundation in how data is collected, stored, transformed, and monitored specifically for machine learning workflows. By reading through practical examples and structured written explanations, you will understand how to design resilient data pipelines that ensure your models always have access to high-quality, up-to-date data.\n\nWhat you'll learn:\n- Understand the core concepts of data engineering and how they support MLOps workflows\n- Design structured data pipelines to ingest, clean, and transform raw data for model training\n- Explore data versioning and storage strategies, including modern vector databases for AI applications\n- Implement automated workflows to orchestrate data movement and maintain data quality\n- Apply monitoring techniques to detect data drift and ensure pipeline reliability\n- Practice writing clean, structured data transformation code with modern tools\n\nThe course begins with essential terminology and foundational data concepts before moving into pipeline architecture, storage paradigms, and orchestration. You will progress from raw ingestion to building automated, production-grade data pipelines tailored for machine learning.\n\nThis course is designed for aspiring data engineers, machine learning enthusiasts, and software developers looking to understand the data side of AI. No prior data engineering experience is required, though a basic familiarity with programming concepts is helpful.\n\nStart building the data foundation necessary for reliable machine learning systems today.