Foundations of AI Governance and Model Monitoring

As organizations rapidly deploy artificial intelligence, ensuring these systems remain reliable, fair, and compliant is more critical than ever. Deploying a model is only the first step; keeping it aligned with business goals and ethical standards requires structured oversight. This course provides a clear pathway to understanding how to govern AI systems effectively. You will learn how to transition from basic data governance to comprehensive AI lifecycle management, ensuring your models are continuously monitored, evaluated, and controlled. What you'll learn: - Understand the core principles of AI governance, risk management, and ethical frameworks. - Monitor AI model performance in production to detect drift, bias, and anomalies. - Implement lifecycle management frameworks to control models from development to retirement. - Establish continuous learning loops and feedback mechanisms for deployed systems. - Collaborate across business and technical teams to reconcile model updates and maintain alignment. - Apply modern observability practices to trace AI decisions and ensure system transparency. The course begins with foundational definitions of AI governance before moving into practical frameworks for risk assessment, performance monitoring, and compliance. You will explore real-world scenarios of model drift and learn how to design continuous improvement processes. This course is designed for business professionals, project managers, and IT leaders looking to establish governance structures for AI, with no prior programming or advanced machine learning experience required. Start building a robust, responsible, and well-governed framework for your organization's AI initiatives today.