Designing Privacy-First Machine Learning Systems

As machine learning systems process increasingly sensitive user data, building privacy-first AI is no longer optional—it is a core engineering requirement. This course helps you navigate the complex intersection of data protection regulations and modern machine learning system design. You will transition from a traditional developer to a privacy-conscious engineer capable of designing systems that protect user identities while maintaining model performance. Through structured written lessons and conceptual walkthroughs, you will learn how to implement privacy-preserving techniques throughout the entire machine learning lifecycle. What you'll learn: - Understand foundational data privacy terminology, regulatory frameworks like GDPR, and the principles of PII handling. - Apply differential privacy techniques to train models without exposing individual user data. - Configure federated learning workflows to train machine learning models across decentralized devices. - Implement machine unlearning protocols to comply with the right to be forgotten in trained models. - Mitigate privacy risks in modern large language models, including data leakage and secure retrieval patterns. - Design secure ML system architectures that incorporate synthetic data generation and secure multi-party computation. The course begins with essential terminology, foundational privacy concepts, and legal compliance frameworks. You will then explore practical technical strategies, from differential privacy to decentralized learning, concluding with modern system design patterns for secure AI. This text-based course is designed for beginning machine learning engineers, data scientists, and system architects who want to build compliant AI systems. No prior experience with privacy engineering is required. Start reading today to build machine learning systems that respect user privacy and meet global compliance standards.