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Module 13 · Page 14 of 20 · 9 sections

Monitoring, Logging and Observability

Seeing inside a running AI system: metrics, logs and traces, what to watch in an AI stack and how to alert without noise.

04Model Training Pipeline10System Components Deep Dive11Reliability, Fault Tolerance and High Availability15MLOps and LLMOps
13.1

1. Introduction

  • Monitoring, logging and observability help us understand the health, performance and behavior of AI systems in real time.
  • They are essential for debugging, performance optimization, reliability and cost management.
  • For AI applications, we need to monitor not only the infrastructure but also model performance, data quality, user behavior and business metrics.
13.2

2. Key Terms

1
Monitoring
Collecting and tracking system metrics.
2
Logging
Recording detailed events and errors.
3
Observability
Ability to understand the internal state of a system using metrics, logs and traces.
4
Metrics
Numerical data (e.g. CPU, latency, token usage).
5
Logs
Event-based records (e.g. errors, requests).
6
Traces
End-to-end request flow across services.
7
Alerting
Notifying when a metric crosses a threshold.
8
Dashboards
Visual representation of system health.
13.3

3. Why is it Important for AI Systems?

  1. 1Detect and fix issues quickly.
  2. 2Ensure high availability and reliability.
  3. 3Monitor model performance and drift.
  4. 4Track cost (tokens, API calls, infrastructure).
  5. 5Improve user experience.
  6. 6Help in compliance and auditing.
  7. 7Understand user behavior and usage patterns.
  8. 8Plan capacity and scaling.
13.4

4. Components of Observability

1
Metrics
System, application and business metrics.
2
Logs
Errors, warnings, events and user activities.
3
Traces
Track requests across multiple services.
4
Dashboards
Visualize key metrics and trends.
5
Alerting
Real-time notifications for anomalies.
6
APM
Application Performance Monitoring: latency, throughput and errors.
7
Model Monitoring
Track model quality, accuracy and drift.
8
User Analytics
Monitor user behavior and feature usage.
13.5

5. Observability Architecture (Example)

AI ApplicationMicroservices: logs, metrics, traces
Data CollectionLog agent (Fluent Bit / Logstash), metrics collector (Prometheus), trace collector (OpenTelemetry)
StorageElasticsearch, Prometheus TSDB, Tempo / Jaeger
VisualizationGrafana, Kibana, Jaeger UI
AlertingAlert Manager, Email / Slack, PagerDuty
13.6

6. Metrics to Monitor (AI Systems)

1
System Metrics
CPU, memory, disk, network.
2
Application Metrics
Request rate, latency, error rate.
3
Model Metrics
Accuracy, relevance, hallucination rate.
4
Token Metrics
Input / output tokens per request.
5
Vector DB Metrics
Query latency, index size, recall rate.
6
Business Metrics
Active users, top queries, conversion rate.
7
Cost Metrics
API cost, infrastructure cost.
8
Custom Metrics
User feedback score, response quality.
13.7

7. Logging Best Practices

  1. 1Use structured logging (JSON format).
  2. 2Include request ID, user ID and session ID.
  3. 3Log important events (API calls, model inference, errors).
  4. 4Avoid logging sensitive data (PII, API keys).
  5. 5Use different log levels (INFO, WARN, ERROR).
  6. 6Centralize logs using ELK (Elasticsearch, Logstash, Kibana).
  7. 7Set a proper log retention policy.
  8. 8Correlate logs with traces for debugging.
13.8

8. Tracing (Distributed Tracing)

  1. 1Tracks the end-to-end request flow across microservices.
  2. 2Helps identify latency and bottlenecks.
  3. 3Useful for debugging complex AI pipelines.
  4. 4Tools: OpenTelemetry, Jaeger, Zipkin.
  5. 5Adds a trace ID to logs for easy correlation.
  6. 6Visualizes service dependencies and timing.
13.9

9. Alerting Strategies

  1. 1Set thresholds for key metrics (CPU, latency, error rate).
  2. 2Use anomaly detection for unusual patterns.
  3. 3Alert on model performance degradation.
  4. 4Monitor cost spikes (token usage, API calls).
  5. 5Use multiple channels (Email, Slack, PagerDuty).
  6. 6Avoid alert fatigue (use meaningful thresholds).
  7. 7Create runbooks for common issues.
  8. 8Use escalation policies for critical alerts.