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
Using Adversarial Validation for Drift Assessment
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- Adversarial Validation: A Method for Robust Machine Learning in the Presence of Dataset Shift, Luca Oneto, Mauro Coraddu, Elena M. J. Coraddu, 2018 Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32 (AAAI Press) DOI: 10.1609/aaai.v32i1.11895 - This paper formally introduces and demonstrates adversarial validation as a technique to detect and quantify dataset shift in machine learning, offering a foundational explanation for its use in drift assessment.
- Machine Learning Design Patterns, Valliappa Lakshmanan, Sara Robinson, Michael Munn, 2020 (O'Reilly Media) - This book provides practical design patterns for machine learning systems, including a dedicated pattern for data drift detection that discusses adversarial validation as a technique in production environments.
- Domain-Adversarial Training of Neural Networks, Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky, 2016 Journal of Machine Learning Research, Vol. 17 (Journal of Machine Learning Research) DOI: 10.5598/jmlr.v17.15-239 - This influential paper introduces domain-adversarial training, a technique that uses a discriminator to distinguish between domains. This method provides the underlying theoretical principle for using a classifier to assess the separability of different datasets in adversarial validation.