Monitoring and Observability

ML Model Performance Monitoring

• What you Need to Know

  • Model Accuracy and Performance Tracking

    • Real-time model performance metrics collection
    • Accuracy degradation detection and alerting
    • Performance comparison across model versions
    • Resources:
      • MLflow Model Registry - Model performance tracking and versioning
      • Evidently AI - ML model monitoring and testing framework
      • Weights & Biases Model Monitoring - Advanced model performance tracking

  • Prediction Quality Assessment

    • Confidence score monitoring and calibration
    • Prediction distribution analysis and outlier detection
    • Ground truth collection and delayed feedback handling
    • Resources:
      • Model Calibration - Probability calibration techniques
      • Prediction Monitoring - ML prediction monitoring guide
      • Alibi Detect - Outlier and drift detection library

  • Business Metrics and KPI Integration

    • Connecting model performance to business outcomes
    • Revenue impact and conversion rate monitoring
    • Customer satisfaction and engagement metrics
    • Resources:
      • Business Metrics for ML - Business impact measurement
      • KPI Monitoring - Business dashboard creation
      • A/B Testing Metrics - Statistical significance in business metrics

Data Drift and Model Drift Detection

• What you Need to Know

  • Statistical Drift Detection Methods

    • Kolmogorov-Smirnov test for distribution changes
    • Population Stability Index (PSI) monitoring
    • Jensen-Shannon divergence for distribution comparison
    • Resources:
      • Data Drift Detection - Statistical drift detection methods
      • PSI Monitoring - Population stability index calculation
      • Drift Detection Survey - Comprehensive drift detection methods

  • Feature Drift Monitoring

    • Individual feature distribution monitoring
    • Multivariate drift detection techniques
    • Feature importance drift and model explanation changes
    • Resources:
      • Feature Drift Analysis - Evidently feature monitoring
      • Multivariate Drift Detection - Maximum Mean Discrepancy for drift
      • Feature Importance Monitoring - Feature importance tracking

  • Concept Drift and Model Degradation

    • Target variable distribution changes
    • Model performance degradation patterns
    • Adaptive learning and model updating strategies
    • Resources:
      • Concept Drift Survey - Comprehensive concept drift analysis
      • Model Degradation Detection - Model performance monitoring
      • Online Learning - Incremental learning framework

Infrastructure and Application Monitoring

• What you Need to Know

  • System Metrics and Resource Monitoring

    • CPU, memory, and GPU utilization tracking
    • Network I/O and storage performance monitoring
    • Container and pod resource consumption
    • Resources:
      • Prometheus Monitoring - Metrics collection and alerting system
      • Grafana Visualization - Metrics dashboards and visualization
      • Kubernetes Metrics - K8s resource monitoring

  • Application Performance Monitoring (APM)

    • Request latency and throughput tracking
    • Error rate monitoring and alerting
    • Distributed tracing for ML service dependencies
    • Resources:
      • Jaeger Tracing - Distributed tracing system
      • OpenTelemetry - Observability framework
      • New Relic APM - Application performance monitoring

  • Log Aggregation and Analysis

    • Centralized logging with ELK stack
    • Structured logging for ML applications
    • Log-based alerting and anomaly detection
    • Resources:
      • Elasticsearch - Search and analytics engine
      • Logstash - Data processing pipeline
      • Kibana - Data visualization and exploration

Alerting and Incident Response

• What you Need to Know

  • Alert Design and Configuration

    • Threshold-based and anomaly-based alerting
    • Alert fatigue prevention and noise reduction
    • Multi-level alerting and escalation policies
    • Resources:
      • Alerting Best Practices - Google's alerting philosophy
      • PagerDuty Alerting - Alert management and escalation
      • Prometheus Alerting - Metrics-based alerting

  • Incident Response for ML Systems

    • ML-specific incident classification and response
    • Model rollback and emergency procedures
    • Post-incident analysis and learning
    • Resources:
      • Incident Response Guide - Incident management best practices
      • SRE Incident Management - Google SRE incident practices
      • ML Incident Response - Twitter's ML infrastructure incidents

  • Runbooks and Automation

    • Automated incident response and remediation
    • Runbook creation and maintenance
    • Chatops integration for incident management
    • Resources:
      • Runbook Automation - Netflix incident management platform
      • Ansible for Incident Response - Automation for incident remediation
      • ChatOps Best Practices - Chat-based operations

Observability for Distributed ML Systems

• What you Need to Know

  • Distributed Tracing for ML Pipelines

    • Request tracing across ML service boundaries
    • Pipeline execution tracing and dependency mapping
    • Performance bottleneck identification
    • Resources:
      • OpenTelemetry Tracing - Distributed tracing instrumentation
      • Jaeger for ML - Tracing ML service interactions
      • Zipkin Documentation - Distributed tracing system

  • Service Mesh Observability

    • Istio telemetry and service communication monitoring
    • Service-to-service authentication and authorization
    • Traffic management and canary deployment monitoring
    • Resources:
      • Istio Observability - Service mesh monitoring
      • Linkerd Observability - Lightweight service mesh monitoring
      • Consul Connect - Service mesh observability

Cost Monitoring and Optimization

• What you Need to Know

  • Cloud Cost Tracking for ML Workloads

    • Resource tagging and cost allocation strategies
    • GPU and compute cost monitoring
    • Training vs inference cost analysis
    • Resources:
      • AWS Cost Explorer - Cloud cost analysis and optimization
      • Google Cloud Billing - GCP cost management and monitoring
      • Azure Cost Management - Azure cost optimization

  • Resource Optimization Strategies

    • Spot instance and preemptible VM usage for training
    • Auto-scaling policies for cost optimization
    • Resource rightsizing and utilization analysis
    • Resources:
      • AWS Spot Instances - Cost-effective compute resources
      • Kubernetes Resource Management - Container resource optimization
      • FinOps for ML - Financial operations for cloud ML

Ready to Automate Infrastructure? Continue to Module 5: Infrastructure Automation to master scalable ML infrastructure, platform engineering, and advanced automation techniques.