Designing Data Infrastructure for Machine Learning Systems

Modern machine learning relies on robust data pipelines, yet setting up the foundational infrastructure can feel overwhelming. This course guides you through the essential concepts of designing scalable data systems specifically tailored for machine learning models. You will transition from understanding basic database concepts to architecting reliable data flows that support real-time ingestion, consistent feature storage, and low-latency serving. By analyzing practical design patterns, you will learn how to make informed decisions about storage, processing, and system reliability. What you'll learn: 1. Understand the core terminology and foundational architecture of machine learning data systems. 2. Design real-time data ingestion pipelines to process streaming data efficiently. 3. Implement feature stores to maintain consistency between training and production environments. 4. Configure low-latency model serving layers to deliver predictions instantly. 5. Explore modern vector databases and retrieval-augmented generation patterns for AI-driven applications. 6. Apply basic MLOps principles to monitor data drift and maintain system health. The course begins with essential definitions and core architectural patterns before guiding you through ingestion, storage, and serving strategies. You will study structured text explanations and architectural walkthroughs to build a complete mental model of modern data infrastructure. This course is designed for aspiring data engineers, software developers, and system architects who are new to machine learning infrastructure, with no prior deployment experience required. Start reading today to master the foundations of scalable machine learning systems.