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📊 Monitoring & Observability

Track, debug, and optimize ML systems in production

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Introduction to ML Monitoring

🎯 Why Monitoring Matters

Production ML systems are dynamic and complex. Models degrade, data distributions shift, and infrastructure issues arise. Comprehensive monitoring enables early detection of problems, root cause analysis, and data-driven optimization. Without it, you're flying blind.

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Key Insight

You can't improve what you don't measure. Monitoring is essential for production ML reliability.

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Early Detection

Catch issues before they impact users

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Root Cause

Debug problems with detailed traces

📈
Optimization

Identify bottlenecks and improve

🏗️ Monitoring Pillars

1
Metrics

Quantitative measurements over time (latency, accuracy, throughput)

2
Logs

Discrete events with context (errors, predictions, inputs)

3
Traces

Request flows through system (end-to-end latency breakdown)

4
Alerts

Notifications when thresholds are breached (automated response)

✅ With Monitoring

  • •Proactive issue detection
  • •Quick incident resolution
  • •Data-driven decisions
  • •Performance optimization

❌ Without Monitoring

  • •Users report problems first
  • •Long debugging cycles
  • •Blind to degradation
  • •No performance insights