Phase 9: Evaluation, Production & Capstone · 50 min · Python · Prometheus · Grafana

Monitoring & Observability

Generation success rates, quality scores, GPU utilization, costs — if you can't see it, you can't operate it.

What you will learn

Track key metrics: generation success rate, average quality score, P95 generation latency, GPU utilization
Monitor quality score trends over time to detect model drift
Monitor API health: provider uptime, rate limit usage, error rates by provider
Implement cost tracking: per-generation cost, daily/monthly spend, budget alerts

Objective

Judge creative outputs with both measurable gates and human critique, then defend the release decision. In the paired studio lab, build a monitoring dashboard for a generative media pipeline that tracks: (1) generation success/failure rates over time, (2) average quality scores by model, (3) GPU utilization and cold start frequency, (4) per-generation cost and daily spend, (5) API health by provider. Use Prometheus for metrics collection and Grafana (or a custom dashboard) for visualization.

The Problem

A generative media platform runs 10,000 generations per day. Without monitoring:

Is the success rate 95% or 60%? (you don't know)
Is quality degrading over time? (you don't know)
Are you over budget? (you find out at month-end)
Is GPU utilization 20% or 80%? (you don't know)

Monitoring and observability transform a black-box system into an operated system.

What you'll build
Track key metrics (success rate, quality scores, P95 latency, GPU utilization), monitor quality trends for model drift, monitor API health (provider uptime, rate limits, errors), and implement cost tracking with budget alerts.

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Key terms in this lesson

Latency