As AI models like Google's Gemini have shown, even the most advanced systems can have spectacular safety failures, leading to brand damage and a loss of user trust. This module teaches you the rigorous, adversarial testing methodologies that professional AI Red Teams use to secure high-stakes applications. By the end of this module, you will be able to not only ensure your LLM behaves safely but also prove that the tests verifying that safety are themselves comprehensive and robust.

If you have ever faced the "it worked on my machine" problem or struggled to reproduce a great result from weeks ago, this course will provide you with the foundational MLOps practices to build a truly auditable and collaborative workflow. The primary goal is to empower you to manage the entire experiment lifecycle with confidence, ensuring that every model you build is reproducible, traceable, and ready for the rigors of production. For learners interested in applying these MLOps skills to the next frontier, this module serves as a perfect foundation for more advanced topics.

Modern ML workflows often involve multiple complex steps—provisioning a GPU, running a training job, and saving the model—all of which are inefficient to perform by hand. This module teaches you how to automate this entire process from end to end using Python. By the end, you will be equipped to transform your manual cloud processes into robust, automated pipelines ready for production.

In this module, you will take on the role of an engineer responsible for ensuring an AI-powered summarization microservice is safe and reliable. Through a hands-on project, you’ll use Python and pytest to build a comprehensive test suite that validates functionality and enforces safety policies. You will write unit tests to confirm the API’s core behavior and then develop critical behavioral tests to ensure the service refuses to generate harmful, illicit, or otherwise non-compliant content. This module will equip you with the practical skills to assert safety refusals, document your test strategy, and integrate your work into a CI pipeline to prevent unsafe code from ever reaching production.