MLOps using AWS Sagemaker Session Slides - Session 5 - Governance and Monitoring
MLOps using AWS Sagemaker Session Slides - Session 5 - Governance and Monitoring
Meetup Link
https://www.meetup.com/dataopslabs/events/292989494/
Summary
The presentation provides a comprehensive exploration of machine learning (ML) governance principles, focusing on accountability, ethics, risk mitigation, fairness, and transparency. These principles form the foundation for responsible development, deployment, and use of ML systems, addressing ethical, legal, and technical considerations.
Within the context of Amazon SageMaker, the presentation introduces key tools for governance. Sagemaker Role Manager simplifies the management of AWS IAM roles for secure resource access. Model Cards streamline documentation of critical ML model details, while the Model Dashboard acts as a centralized portal for tracking and exploring all models, incorporating real-time performance through Model Monitoring.
Model Monitoring and Governance Features take center stage, showcasing capabilities such as monitoring model behavior, ensuring data and model quality, and configuring alerts based on specific thresholds. The Model Dashboard, as a central hub, offers users a comprehensive view of model metrics, including data from various SageMaker features like model cards and endpoint performance.
The presentation also highlights the importance of specifying a model's intended uses to ensure responsible development. Risk ratings, categorized as unknown, low, medium, or high, facilitate compliance with rules and regulations, particularly when deploying models with varying risk levels. The structured JSON schema for model cards ensures standardised documentation, incorporating metrics from SageMaker Clarify or Model Monitor for a consistent approach across models.
