Monitoring and Observability

Monitoring and Observability¶

graph LR
    A[Model Inference] --> B[Metrics Collection]
    B --> C[Time Series DB]
    C --> D[Alerting]
    C --> E[Dashboards]
    C --> F[Analytics]

    subgraph Metrics Pipeline
    B
    C
    end

    subgraph Visualization
    D
    E
    F
    end

Key Metrics¶

System Metrics:
- Resource utilization
- Response times
- Error rates
- Queue lengths
Model Metrics:
- Inference quality
- Token usage
- Cache hit rates
- Model drift indicators
Business Metrics:
- Cost per request
- User satisfaction
- Feature usage
- Business impact

Monitoring Tools¶

Prometheus

Industry-standard metrics collection and alerting.

Grafana

Visualization and dashboarding for operational metrics.

Weights & Biases

ML-specific monitoring and experiment tracking.

Logging and Tracing¶

Structured Logging:
- Request/response logging
- Error tracking
- Performance logging
- Audit trails
Distributed Tracing:
- Request flow tracking
- Bottleneck identification
- Service dependencies
- Performance profiling

Monitoring Architecture¶

graph TB
    Apps[Applications] --> Collectors[Metric Collectors]
    Models[ML Models] --> Collectors
    Infra[Infrastructure] --> Collectors

    Collectors --> TSDB[Time Series DB]
    TSDB --> Dashboards[Dashboards]
    TSDB --> Alerts[Alert Manager]
    TSDB --> Analytics[Analytics Engine]

    subgraph Visualization
    Dashboards
    Analytics
    end

    subgraph Actions
    Alerts --> Notifications[Notifications]
    Alerts --> AutoRemediation[Auto Remediation]
    end

Key Metrics Categories¶

Infrastructure Metrics¶

Resource utilization (CPU, Memory, GPU)
Network throughput and latency
Storage performance
Container health
Cluster metrics

Application Metrics¶

Request rates and patterns
Response times (p50, p90, p99)
Error rates and types
Queue lengths
Cache hit rates

Model Metrics¶

Inference latency
Token usage
Model accuracy
Prediction confidence
Feature distribution
Model drift indicators

Business Metrics¶

Cost per request
User satisfaction scores
Feature usage patterns
Business impact metrics
SLA compliance

Monitoring Tools¶

Metrics Collection¶

Prometheus

Industry-standard metrics collection system.

OpenTelemetry

Open-source observability framework.

Visualization¶

Grafana

Advanced visualization and dashboarding.

Kibana

Analytics and visualization platform.

ML-Specific Monitoring¶

Weights & Biases

ML experiment tracking and monitoring.

MLflow

End-to-end ML lifecycle platform.

Observability Practices¶

Logging Strategy¶

graph LR
    App[Application] --> Struct[Structured Logging]
    Struct --> Parse[Log Parsing]
    Parse --> Index[Log Indexing]
    Index --> Search[Search/Analysis]

    subgraph Log Pipeline
    Struct
    Parse
    Index
    end

Log Levels¶

ERROR: System failures
WARN: Potential issues
INFO: Normal operations
DEBUG: Detailed debugging
TRACE: Fine-grained details

Log Components¶

Timestamp
Request ID
User context
Operation details
Performance metrics
Error details

Tracing Implementation¶

Jaeger

End-to-end distributed tracing.

Request flow tracking
Service dependencies
Performance bottlenecks
Error propagation
Resource attribution

Alerting Strategy¶

Alert Categories¶

Critical: Immediate action required
Warning: Investigation needed
Info: Awareness only

Alert Components¶

Alert condition
Severity level
Resolution steps
Contact information
Escalation path

Best Practices¶

Data Collection¶

Use structured logging
Implement distributed tracing
Collect business metrics
Monitor user experience
Track resource usage

Data Storage¶

Time series optimization
Data retention policies
Storage scaling
Backup strategies
Access controls

Visualization¶

Real-time dashboards
Historical trends
Correlation analysis
Custom views
Export capabilities

Alert Management¶

Clear severity levels
Actionable alerts
Proper routing
Escalation procedures
Alert fatigue prevention

Advanced Topics¶

Automated Analysis¶

Anomaly detection
Pattern recognition
Predictive analytics
Root cause analysis
Capacity planning

Integration Points¶

CI/CD pipelines
Incident management
Change management
Resource provisioning
Cost optimization

Security Monitoring¶

Access patterns
Authentication events
Authorization checks
Data access logs
Security incidents

Troubleshooting Guide¶

Common Issues¶

High latency
Error spikes
Resource exhaustion
Model degradation
System failures

Resolution Steps¶

Identify symptoms
Collect relevant metrics
Analyze patterns
Determine root cause
Implement fix
Verify resolution
Document findings

Prevention Strategies¶

Proactive monitoring
Regular health checks
Capacity planning
Performance testing
Disaster recovery