Model Monitoring & Drift Detection
Build practical skills in model monitoring & drift detection and apply key concepts, tools, and techniques to real-world business and technology scenarios.
Course Overview
Course Overview Module 1: Core Concepts • Introduction to Model Monitoring & Drift Detection • Maintaining model performance over time • ML lifecycle and production concepts • Implementation and automation workflow Module 2: Tools & Techniques • ML lifecycle and production concepts • Implementation and automation workflow • Monitoring, reliability and governance • Practical tools and deployment scenarios Module 3: Practical Implementation • ML lifecycle and production concepts • Implementation and automation workflow • Monitoring, reliability and governance • Practical tools and deployment scenarios Module 4: Applied Skills • ML lifecycle and production concepts • Implementation and automation workflow • Monitoring, reliability and governance • Practical tools and deployment scenarios Module 5: Real-World Application • Monitoring, reliability and governance • Practical tools and deployment scenarios • Real-world business use cases • Best practices and key takeaways
Learning Outcomes
Understand the core concepts and terminology of Model Monitoring & Drift Detection.
Apply relevant tools, techniques, and workflows in practical scenarios.
Analyze common problems and select appropriate approaches.
Work through real-world examples and business use cases.
Apply best practices to improve quality, efficiency, and outcomes.
Prerequisites
• Basic computer and internet skills. • Basic understanding of the relevant business or technology context. • Logical and analytical thinking. • Willingness to participate in practical exercises. • No prior experience in the specific course topic is required unless otherwise stated.
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