Data Collection and Ingestion for Scalable Machine Learning Systems

Every successful machine learning system relies on a steady, high-quality stream of data, yet building scalable ingestion pipelines remains one of the greatest challenges in AI engineering. This text-based course guides you through the foundational principles of designing cost-effective, compliant, and highly scalable data collection strategies. You will transition from manual data handling to architecting automated, event-driven data pipelines. By reading through structured explanations and analyzing real-world architectural patterns, you will understand how to balance signal quality, storage costs, and data privacy regulations. What you'll learn: - Understand foundational data collection terminology, storage types, and ingestion patterns for machine learning. - Design event-driven data pipelines to ingest high-velocity streaming data. - Evaluate data sources based on signal quality, collection costs, and system latency. - Apply modern data privacy and compliance standards, including GDPR and CCPA, to data storage. - Configure ingestion pipelines suitable for modern vector databases and retrieval-augmented generation (RAG) architectures. - Analyze cost-efficient storage strategies across data lakes and cloud warehouses. The course begins with core definitions and data theory before moving into pipeline architectures, stream processing, and modern compliance frameworks. Through clear text-based explanations and conceptual walkthroughs, you will master the mechanics of data preparation for production-grade ML. This course is designed for aspiring data engineers, ML practitioners, and software developers looking to build a strong foundation in data collection, with no advanced machine learning or system architecture prerequisites required. Start reading today to master the pipelines that power modern AI systems.