Monitoring and Managing ML Models in Production
Chapter 1: Foundations of Production ML Monitoring Systems
Unique Challenges of Monitoring ML Models
Monitoring Scope: Data, Predictions, Performance, Infrastructure
Service Level Objectives (SLOs) for ML Models
Architectural Patterns for Monitoring Systems
Integrating Monitoring into the MLOps Lifecycle
Chapter 2: Advanced Drift Detection Techniques
Limitations of Basic Statistical Tests for Drift
Multivariate Data Drift Detection Methods
Sequential Analysis for Faster Drift Detection
Concept Drift Detection Strategies
Using Adversarial Validation for Drift Assessment
Monitoring Drift in Embeddings and Unstructured Data
Implementing Custom Drift Detection Logic
Hands-on practical: Multivariate Drift Implementation
Chapter 3: Granular Performance Monitoring and Diagnostics
Selecting Appropriate Performance Metrics
Monitoring Performance on Data Slices and Segments
Techniques for Monitoring Model Fairness and Bias
Analyzing the Impact of Outliers and Anomalies
Root Cause Analysis for Performance Degradation
Using Explainability Methods (SHAP, LIME) for Diagnostics
Practice: Diagnosing Performance Issues with Explainability
Chapter 4: Automated Retraining and Model Update Strategies
Designing Retraining Triggers: Thresholds vs. Events
Data Strategies for Retraining: Windows, Incremental, Full
Automated Validation of Candidate Models
Online Learning Systems vs. Batch Retraining
Advanced Deployment Patterns: Canary and Shadow Testing
Implementing Automated Rollback Mechanisms
Hands-on practical: Building an Automated Retraining Trigger
Chapter 5: Infrastructure and Tooling for Scalable Monitoring
Logging Strategies for High-Volume Prediction Services
Using Time-Series Databases for Monitoring Metrics
Distributed Architectures for Monitoring Pipelines
Integrating with MLOps Platforms: Kubeflow, MLflow, Sagemaker
Specialized ML Monitoring Tools and Services
Building Effective Monitoring Dashboards and Alerts
Practice: Monitoring Setup with MLflow and Grafana
Chapter 6: Managing Model Governance and Compliance in Production
Advanced Model Versioning and Lineage Tracking
Establishing Audit Trails for Predictions and Model Updates
Monitoring Explainability and Interpretability Over Time
Data Privacy Considerations in Monitoring Data
Access Control and Security for Monitoring Systems
Integrating Monitoring with Model Risk Management Frameworks
Hands-on practical: Implementing Model Registry Hooks for Governance
Selecting Appropriate Performance Metrics
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- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, 2019 (O'Reilly Media) - A practical guide to implementing machine learning, including detailed explanations of common performance metrics for classification and regression tasks.
- Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, Chip Huyen, 2022 (O'Reilly Media) - This book addresses the practical considerations for designing and operating ML systems in production, with a focus on selecting and monitoring appropriate metrics for business impact.
- The Relationship Between Precision-Recall and ROC Curves, Jesse Davis, Mark Goadrich, 2006 Proceedings of the 23rd International Conference on Machine Learning (ICML) (ACM) DOI: 10.1145/1143844.1143874 - A foundational paper discussing the advantages of Precision-Recall curves over ROC curves for evaluating classifiers, particularly with imbalanced datasets.
- CS229 Lecture Notes: Supervised Learning, Generative Learning Algorithms, Support Vector Machines, Andrew Ng and Stanford University, 2023 (Stanford University) - Stanford University's Machine Learning course notes covering foundational machine learning concepts, including detailed discussions of model evaluation metrics and their applications.