AI Model Testing and Troubleshooting for Continuous Maintenance

Deploying an AI model is only the beginning of its lifecycle; maintaining its accuracy and fairness over time is where the real challenge lies. As real-world data changes, models drift and performance can degrade without proper oversight. This text-based course equips you with the fundamental skills to monitor, test, and troubleshoot AI models in production. You will transition from understanding basic model evaluation to implementing robust maintenance strategies that protect system performance and uphold ethical standards. What you'll learn: Understand core terminology of post-deployment AI maintenance and MLOps telemetry; Identify and mitigate data drift and concept drift in production environments; Establish systematic testing workflows to evaluate model performance and bias; Troubleshoot common failure modes in modern AI systems, including large language models; Implement ethical monitoring frameworks to ensure fairness and prevent algorithmic bias; Formulate a continuous maintenance plan to keep AI products reliable and accurate over time. You will start with foundational concepts of AI model lifecycles before moving into practical monitoring techniques, drift detection, and troubleshooting workflows. Through clear written explanations and realistic scenarios, you will learn how to maintain AI systems systematically. This course is designed for beginning AI product managers, QA specialists, and developers looking to understand post-deployment maintenance. No advanced programming or machine learning background is required. Start reading today to master the essentials of keeping your AI models reliable and ethical in production.