Designing Feature Pipelines for ML Systems: Batch and Real-Time

Machine learning models are only as good as the data fed into them, but building the systems that deliver this data is one of the biggest challenges in AI engineering today. Knowing how to design and choose between batch, streaming, and real-time feature pipelines is critical for building reliable, production-ready machine learning applications. This text-based course guides you through the foundational architecture of feature engineering pipelines. You will transition from writing basic data-prep scripts to understanding how scalable, production-grade systems ingest, transform, and serve features at scale, preparing you to make informed architectural decisions for real-world applications. What you'll learn: - Understand the core terminology, definitions, and essential components of modern ML feature pipelines. - Compare batch, streaming, and real-time feature ingestion methods to balance freshness, cost, and system complexity. - Explore the role of feature stores in preventing training-serving skew and promoting feature reuse across teams. - Analyze modern data patterns, including basic data contracts and integration with vector databases for AI applications. - Evaluate real-world system design trade-offs through structured written scenarios and architectural case studies. You will start with the absolute basics of feature engineering terminology before advancing through detailed written breakdowns of batch and streaming architectures. Through practical text-based exercises, you will learn to analyze system trade-offs and design robust data flows for machine learning. This course is designed for aspiring machine learning engineers, data engineers, and software developers who are new to ML system design. No advanced infrastructure experience is required. Start reading today to build a solid foundation in machine learning system design.