Monitoring and Observability

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

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

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