Design success metrics, evaluation, and monitoring for an audio detection system

Context

You are defining the measurement, evaluation, and rollout plan for an audio detection system that flags policy-violating content in user-generated audio at scale. The system supports near-real-time moderation (streaming) and batch reprocessing, and outputs per-class violation scores (multi-label) for each audio clip or segment.

Assume:

• Multiple violation classes (e.g., hate/harassment, sexual content, self-harm, IP infringement, spam), with high class imbalance and multi-language input.
• Human review is available for borderline cases and appeals.
• Both product impact and ML quality must be measured, alongside operational SLOs and cost.

Task

Define the metrics, evaluation plan, monitoring/alerting, and safe rollout strategy:

1. Product and ML metrics (precision/recall, per-class FP/FN rates, manual-review yield, inter-rater agreement, etc.).
2. System/ops metrics (batch/streaming latency SLOs, throughput, queue depths, failure rates, cost per hour of audio).
3. Alert thresholds and dashboards.
4. Sampling and canary strategies for new models/thresholds.
5. How to run A/B tests or shadow evaluations before full rollout.