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
Automated Validation of Candidate Models
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- Introducing MLOps: How to go from Model to Production, Mark Treveil, Nicolas Omont, Aurélien Géron, Vincent Warmerdam, Artem Gorodetsky, Deepak Agarwal, Antoine de Mathelin, Clemens Mewald, Michel Pelletier, Mike Tung, Alexey Grishchenko, Adam Kelliher, 2020 (O'Reilly Media) - This book provides a comprehensive guide to the MLOps lifecycle, with dedicated sections on automated model validation, continuous integration for machine learning, and strategies for deploying models reliably in production.
- MLOps: Continuous delivery and automation pipelines in machine learning, Evgeniia Tokarchuk, Haryo F. Handoyo, Robert M. Lee, Stephen W. White, Valliappa Lakshmanan, 2021 (Google Cloud) - This Google Cloud whitepaper outlines best practices for MLOps, focusing on continuous delivery, automated model validation within pipelines, and the operational aspects of managing ML models in production.
- Fairness and Machine Learning: Limitations and Opportunities, Solon Barocas, Moritz Hardt, Arvind Narayanan, 2023 (MIT Press) - An online book offering a detailed academic treatment of fairness in machine learning, covering various definitions of fairness, relevant metrics, and methods for identifying and mitigating algorithmic bias in ML systems.
- Reliable Machine Learning: Applying SRE Principles to ML in Production, Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood, 2022 (O'Reilly Media) - This book explores how Site Reliability Engineering (SRE) principles apply to machine learning, covering aspects like robust testing, continuous model validation, and building resilient ML systems for production environments.