Managing AI Systems: Development, Deployment, and Governance

This course introduces the foundational practices required to design, develop, and manage AI systems responsibly in regulated and high-stakes environments. Learners explore how to integrate governance into every stage of the AI lifecycle, ensuring that models are transparent, accountable, and audit-ready from development through deployment and monitoring. The course emphasizes building structured governance checkpoints, defining clear accountability using frameworks like RACI, and aligning technical workflows with regulatory expectations such as the NIST AI Risk Management Framework and the EU AI Act. Learners will also develop practical skills in explainable AI, applying techniques like SHAP and LIME to generate reliable, instance-level insights and communicate them effectively to stakeholders, including regulators, executives, and customers. In addition, the course covers audit-ready documentation practices, including model traceability, version control, and the creation of structured audit reports that synthesize lifecycle evidence into governance-ready artifacts. By the end of the course, learners will be able to design AI systems that not only perform well technically but also withstand compliance review, support risk management, and build organizational trust.

This course provides a structured, practitioner-focused approach to identifying, managing, and governing risks in AI systems across their lifecycle. It equips learners with the tools to move beyond model performance and address real-world concerns such as bias, model degradation, regulatory exposure, and operational accountability. Learners begin by diagnosing bias in datasets and models, applying fairness metrics, and conducting audits that reveal hidden disparities across demographic groups. The course then advances to bias mitigation, where participants explore practical techniques across the model pipeline and learn to navigate trade-offs between fairness and performance. The course expands into production environments, teaching how to design monitoring pipelines that detect data drift, concept drift, and performance degradation before they impact business outcomes. Learners connect these monitoring signals to structured risk evaluation frameworks, translating technical anomalies into enterprise risk language using scoring models, risk registers, and response strategies aligned with standards such as ISO 31000 and COSO ERM. Finally, the course integrates AI systems into broader governance and compliance structures. Participants learn to map AI use cases to regulatory obligations (e.g., GDPR, EU AI Act), build compliance inventories, and design governance dashboards that support audit readiness and executive oversight. By the end of the course, learners will be able to operationalize AI risk management, implement continuous monitoring, prioritize and respond to model risks, and align AI systems with organizational and regulatory expectations.