Deploying and monitoring models effectively addresses technical performance, but production ML systems also operate within organizational and regulatory frameworks. This chapter concentrates on the governance and compliance structures necessary for managing models responsibly once they are live.
We will examine practical techniques for maintaining control and accountability. You will learn how to implement comprehensive versioning for models, data, and code to ensure accurate lineage tracking. We will cover establishing detailed audit trails for predictions and model management actions, a frequent requirement for operational transparency and compliance. Additionally, you'll learn methods to track changes in model explanations over time and address data privacy considerations when handling monitoring data. Finally, this chapter discusses securing the monitoring infrastructure and integrating monitoring outputs with organizational risk management frameworks.
6.1 Advanced Model Versioning and Lineage Tracking
6.2 Establishing Audit Trails for Predictions and Model Updates
6.3 Monitoring Explainability and Interpretability Over Time
6.4 Data Privacy Considerations in Monitoring Data
6.5 Access Control and Security for Monitoring Systems
6.6 Integrating Monitoring with Model Risk Management Frameworks
6.7 Hands-on practical: Implementing Model Registry Hooks for Governance
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