Overview

This course is designed for software engineers and ML practitioners aiming to advance from building LLM prototypes to deploying robust, production-grade AI systems. In the real world, a reliable application requires more than a clever prompt; it demands a rigorous software engineering foundation to ensure its testability, maintainability, and safety. This course provides that critical toolkit. You will learn to apply Test-Driven Development (TDD) to methodically build and refactor LLM-powered microservices, ensuring that your code is clean and verifiable from day one. To safeguard your applications, you will create sophisticated behavioral test suites that enforce safety policies and prevent undesirable outputs. You'll go a step further by using mutation testing to evaluate the quality of your own tests, ensuring that your safety guardrails are truly effective. The course also dives into the MLOps lifecycle, teaching you to version datasets and models with DVC, track experiment results on platforms like W&B, and make data-driven decisions about the models to promote. Finally, you will learn to automate your entire testing and evaluation workflow using powerful Python scripts, thereby preparing your application for seamless integration into a CI/CD pipeline.

Syllabus

Refactor and Test LLM Microservices

Rapid AI development often creates "technical debt," resulting in brittle, costly systems. This module shifts focus from basic scripts to professional software engineering for production-grade microservices. You will master Test-Driven Development (TDD), writing unit tests first to ensure reliability. The curriculum emphasizes code reviews and systematic refactoring, teaching you to transform monolithic code into clean, maintainable modules. Through hands-on VS Code labs, you will refactor legacy services and build new API endpoints, gaining the skills to deliver scalable, robust, and professional AI applications.

Safeguard LLM Outputs: Test and Evaluate

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.

Track and Evaluate ML Model Experiments

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.

Automate Cloud Workflows with Python Scripting

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.

Adding Safety Guardrails to an LLM Service

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.