Production ML Engineering: Packaging, APIs, and Testing

Production ML Engineering: Packaging, APIs, and Testing focuses on transforming machine learning models into reliable production systems. In this course, you will learn how to package, deploy, document, and test machine learning applications so they can operate reliably in real-world environments. You will begin by creating reusable Python packages that organize machine learning code into maintainable modules. Next, you will learn how to build production-ready machine learning APIs that allow models to be accessed by applications and services. The course also introduces best practices for code review, version control, and CI/CD workflows used in modern ML engineering. As the course progresses, you will develop technical documentation that explains model architectures, training workflows, and API usage to support collaboration across teams. Finally, you will design automated testing strategies that validate machine learning pipelines and ensure reliable model outputs. By the end of the course, you will be able to package machine learning systems, deploy ML APIs, document AI systems, and implement automated testing workflows for production environments. Tools used in this course include Python, API frameworks, CI/CD pipelines, automated testing tools, and MLOps workflows.