Model Governance
Model Governance is a systematic framework for managing the lifecycle of machine learning and AI models, ensuring they are developed, deployed, and maintained responsibly, ethically, and in compliance with regulations. It involves establishing policies, processes, and controls to oversee model risk, performance, fairness, and transparency across an organization. This methodology helps organizations mitigate risks associated with AI, such as bias, drift, and security vulnerabilities, while promoting accountability and trust.
Developers should learn and implement Model Governance when building or deploying machine learning models in regulated industries (e.g., finance, healthcare) or in contexts where model decisions have significant impacts, such as hiring, lending, or autonomous systems. It is essential for ensuring models meet ethical standards, comply with laws like GDPR or AI regulations, and maintain performance over time, reducing operational risks and building stakeholder confidence. Use cases include auditing models for bias, tracking model versions, and documenting data lineage.