Evaluating AI Systems: Offline and Online Testing Workflows

Deploying AI models without a robust evaluation strategy often leads to unexpected failures in production. To build truly reliable systems, you must understand how to measure performance both before and after your model meets real users. This written course guides you through the foundational principles of AI evaluation, helping you bridge the gap between development datasets and live production environments. You will learn how to design rigorous offline testing suites, transition smoothly to online monitoring, and set up continuous feedback loops that keep your AI systems performing optimally over time. What you'll learn: - Understand the fundamental terminology and core differences between offline and online AI evaluation. - Design offline validation strategies using holdout datasets, cross-validation, and targeted test cases. - Apply modern evaluation metrics for predictive models and generative AI systems, including basic LLM-as-a-judge patterns. - Configure online testing methodologies such as A/B testing, shadow deployments, and canary releases. - Monitor production AI systems to detect data drift, concept drift, and performance degradation. - Establish a continuous evaluation workflow that connects pre-deployment testing with real-time user feedback. You will begin by learning core evaluation terminology and statistical foundations before diving into practical offline testing setups. From there, the text covers real-world deployment strategies and ongoing monitoring techniques to ensure your models remain reliable in production. This course is designed for aspiring AI engineers, data scientists, and product developers who want to understand how to validate AI systems. No advanced programming or machine learning background is required to get started. Start reading today to build trust and reliability in your AI deployments.