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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

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 and Managing ML Models in Production

This course provides advanced strategies and techniques for monitoring, managing, and maintaining machine learning models after deployment. Learn to implement robust systems for detecting drift, tracking performance degradation, automating retraining, and integrating with MLOps toolchains to ensure model reliability and effectiveness in production environments.

Prerequisites ML Deployment & Python

Level:

Advanced

Advanced Monitoring Implementation
Implement sophisticated monitoring systems tailored for production machine learning models.
Drift Detection
Apply advanced techniques to detect data drift, concept drift, and model performance decay.
Automation
Design and implement automated model retraining, validation, and deployment pipelines.
MLOps Integration
Integrate monitoring solutions effectively within existing MLOps platforms and workflows.
Model Lifecycle Management
Manage model versions, implement safe deployment patterns, and establish rollback strategies.
Performance Analysis
Analyze model performance degradation using granular metrics and perform root cause analysis.

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