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
Monitoring Drift in Embeddings and Unstructured Data
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- A Kernel Method for the Two-Sample Problem, Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander Smola, 2012 Journal of Machine Learning Research, Vol. 13 (JMLR) DOI: 10.1162/jmlr.2012.13.1.723 - This paper formally introduces Maximum Mean Discrepancy (MMD) as a non-parametric two-sample test, a fundamental tool for comparing high-dimensional distributions.
- Sliced Wasserstein Distance for Learning Models, Nicolas Bonneel, Mathieu Coeurjolly, Jean-Denis Durou, Florian Le Cun, 2015 ACM Transactions on Graphics (TOG), Vol. 34 (Association for Computing Machinery) DOI: 10.1145/2816795.2818139 - This paper explores the Sliced Wasserstein Distance, an efficient approximation of the Earth Mover's Distance, making it more computationally feasible for high-dimensional data like embeddings.