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This course presents principles, patterns, and techniques for monitoring machine learning models in production. It uses a hands-on image-classification example deployed as a Kubernetes microservice to demonstrate performance metrics, outlier and concept-drift detection, explainability, and scalable monitoring architecture.
Syllabus
Introduction
Welcome
Overview
Motivations
Practical Use Case
Production Use Case
Deployment
Microservice
Machine Learning Monitoring Anatomy
Performance Monitoring Principles
Performance Monitoring Patterns
Performance Monitoring Metrics
Metric Servers
Outlier and Drift
Albedo Detect
Outlier Detect
Drift Detect
Outlier Detector
Explainability
AlibiExplain
AlibiDetect
Architectural Patterns
Summary
Outro
Taught by
Open Data Science