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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

Beyond Accuracy: 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

Granular Performance Monitoring and Diagnostics

Detecting operational issues like data drift is only part of the story. Understanding the precise impact on your model's effectiveness and identifying why performance changes requires a more detailed view. This chapter concentrates on techniques for granular performance monitoring and diagnostics.

You will learn to select metrics appropriate for your specific task, moving beyond simple accuracy ( $A$ ) to metrics like precision ( $P$ ) and recall ( $R$ ). We will cover methods for assessing performance not just overall, but across specific segments or slices of your data to find hidden weaknesses. Additionally, we'll examine approaches for monitoring model fairness, analyzing the influence of outliers, performing systematic root cause analysis when performance degrades, and applying model explainability methods (like SHAP or LIME) as diagnostic tools in a production setting.

Sections

3.1 Beyond Accuracy: Selecting Appropriate Performance Metrics
3.2 Monitoring Performance on Data Slices and Segments
3.3 Techniques for Monitoring Model Fairness and Bias
3.4 Analyzing the Impact of Outliers and Anomalies
3.5 Root Cause Analysis for Performance Degradation
3.6 Using Explainability Methods (SHAP, LIME) for Diagnostics
3.7 Practice: Diagnosing Performance Issues with Explainability

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