ML Solution Monitoring and Maintenance
Description
Comprehensive monitoring, maintenance, and operational management of production ML systems using AWS monitoring and management tools.
Learning Objectives
Learners will master production ML system monitoring including model performance tracking, data drift detection, infrastructure monitoring, and automated alerting. They will understand how to implement comprehensive observability, troubleshoot production issues, manage model lifecycle, and ensure system reliability using tools like CloudWatch, SageMaker Model Monitor, and SageMaker Clarify for ongoing model governance and maintenance.
Topics (11)
Advanced performance monitoring including custom metrics creation, baseline establishment, performance degradation detection, and business KPI correlation for ML systems.
Comprehensive drift detection including statistical tests, distribution comparison, concept drift identification, and automated retraining triggers for production models.
Advanced Model Monitor setup including baseline creation, monitoring schedule configuration, constraint validation, and integration with alerting systems.
Comprehensive CloudWatch usage including custom metric publishing, dashboard creation, alarm setup, and log analysis for ML infrastructure and application monitoring.
Advanced infrastructure monitoring including resource utilization tracking, performance bottleneck identification, capacity planning, and cost monitoring for ML infrastructure.
Comprehensive alerting strategy including threshold configuration, escalation procedures, automated remediation, and incident management workflows for ML operations.
Advanced bias monitoring including bias metric calculation, fairness assessment, explainability analysis, and continuous bias monitoring in production systems.
Comprehensive governance including compliance tracking, audit trail maintenance, model documentation, regulatory requirement monitoring, and governance workflow automation.
Advanced capacity planning including demand forecasting, resource optimization, predictive scaling, and cost-effective resource allocation for ML systems.
Systematic improvement processes including performance analysis, optimization identification, feedback loop implementation, and continuous enhancement methodologies for ML operations.
Advanced troubleshooting including log aggregation, pattern analysis, root cause analysis methodologies, and debugging techniques for production ML systems.