Inference meta-monitoring for Amazon SageMaker AI endpoints with Amazon Quick
Amazon Web Services
Evidently AI
This article from AWS ML blog introduces an inference meta-monitoring system for Amazon SageMaker AI endpoints. It provides a governance layer to continuously track prediction and data quality metrics, using AWS managed services and open-source tools like Evidently AI. The system includes drift detection, delayed ground truth integration, and automated performance dashboards.
The AWS ML blog post presents a solution for inference meta-monitoring for Amazon SageMaker AI endpoints, addressing the gap where model performance degrades silently. The system combines AWS managed services (SageMaker AI, Athena, Lambda, EventBridge, Quick) with open-source tools (SageMaker AI MLflow Apps, Evidently AI). It includes training, deployment, and monitoring pipelines integrated through a central Athena Iceberg table. The drift Lambda function resolves the current model and loads its baseline.json to compute data drift and model drift using Evidently's presets. It also integrates delayed ground truth data and provides automated dashboards for visualizing trends.
- Сокращения
- AWS = Amazon Web Services — Amazon Web Services
- ML = Machine Learning — машинное обучение
- VPC = Virtual Private Cloud — виртуальное частное облако
- S3 = Simple Storage Service — сервис хранения данных
- SQS = Simple Queue Service — сервис очередей
- CSV = Comma-Separated Values — значения, разделённые запятыми
- KS = Kolmogorov-Smirnov test — тест Колмогорова-Смирнова
- ROC-AUC = Receiver Operating Characteristic - Area Under the Curve — ROC-кривая и площадь под кривой
- F1 = F1 score — F1-мера
Source: AWS ML blog —
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