This course contains the use of artificial intelligence. Led by Dr. Amar Massoud, a seasoned expert with decades of academic and professional experience, it combines cutting-edge AI support with human insight to deliver content that is precise, practical, and easy to follow. You’ll gain the clarity of structured learning and the confidence of being guided by a recognized authority.

In today's fast-moving AI landscape, building systems that are simply functional is no longer enough. AI products must be robust, secure, explainable, and reliable—ready to perform in dynamic environments and meet user, business, and regulatory expectations. This course is designed to guide you step-by-step through the process of designing, validating, and hardening AI systems so they can thrive in the real world.

You'll start by understanding what makes an AI product truly resilient—examining real-world risks and failure scenarios. You’ll explore how to assess data quality, detect bias, and ensure dataset diversity through hands-on exercises.

Then, we take you deep into AI supply chain risks—understanding how third-party models, pre-trained components, and model updates can pose hidden challenges. Through case studies, you’ll see what can go wrong, and how to build smarter safeguards.

We’ll introduce you to robust model testing strategies: slicing datasets, benchmarking across use cases, and validating generalization. Next, you’ll step into the world of AI red teaming and adversarial training, where you'll learn to simulate attacks and make your systems tougher by design.

In the final module, we cover compliance, secure deployment, and continuous improvement. By the end of this course, you’ll apply all the lessons learned in a capstone project: building a roadmap for launching a resilient AI product at scale.

Whether you’re a tech leader, product manager, data scientist, or AI engineer, this course gives you practical tools to build better AI—faster, safer, and more confidently.