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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

Infrastructure and Tooling for Scalable Monitoring

Monitoring machine learning models in production generates significant amounts of data and requires systems that can operate reliably under load. Having identified what to monitor – from data drift to performance degradation – the focus now shifts to the practical aspects of building and managing the infrastructure needed to support these monitoring activities effectively at scale.

This chapter addresses the engineering challenges involved. You will learn about:

Strategies for logging prediction data and monitoring outputs in high-volume environments.
Using time-series databases ( $TSDBs$ ) specifically designed for handling timestamped metric data efficiently.
Designing distributed system architectures for monitoring pipelines that can scale horizontally.
Integrating your monitoring components with common MLOps platforms like Kubeflow, MLflow, and SageMaker.
An overview of specialized open-source and commercial tools available for ML monitoring.
Techniques for creating informative dashboards and setting up meaningful alerts to stay informed about model health.

We will examine how to select and configure these components to create a monitoring system tailored to the demands of production machine learning.

Sections

5.1 Logging Strategies for High-Volume Prediction Services
5.2 Using Time-Series Databases for Monitoring Metrics
5.3 Distributed Architectures for Monitoring Pipelines
5.4 Integrating with MLOps Platforms: Kubeflow, MLflow, Sagemaker
5.5 Specialized ML Monitoring Tools and Services
5.6 Building Effective Monitoring Dashboards and Alerts
5.7 Practice: Monitoring Setup with MLflow and Grafana

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