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LinkedIn Learning

MLOps Essentials: Monitoring Model Drift and Bias

via LinkedIn Learning

Overview

Learn about the growing field of MLOps and the modeling techniques used to monitor model drift and bias.

Syllabus

Introduction
  • The need for model monitoring
  • Setting up the exercise files
1. Introduction to Model Monitoring
  • ML models in production
  • Challenges with serving models in production
  • Metrics to monitor
  • Data for model monitoring
2. Model Drift Basics
  • Introduction to model drift
  • Concept drift
  • Feature drift
  • What causes drift?
  • Drift remediation process
3. Detecting Model Drift
  • Detecting concept drift
  • Concept drift detection example
  • Detecting feature drift
  • Feature drift detection example
  • Detecting drift in text and images
  • Software for drift detection
4. Drift Monitoring Process and Best Practices
  • Drift monitoring pipeline
  • Analyzing drift trends
  • Discovering root causes for drift
  • Retraining to overcome drift
5. Introduction to Model Bias
  • Fairness and bias
  • Fairness in ML
  • Sources of ML bias
  • Protected attributes
  • Demographic parity
6. Bias Detection and Best Practices
  • Bias detection techniques
  • Equal opportunity score
  • EOS example
  • Bias detection software
  • Overcoming bias in ML
Conclusion
  • Next steps

Taught by

Kumaran Ponnambalam

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