Define success metrics and monitoring
Overview
This question evaluates a candidate's competency in analytics and experimentation for ML-driven content moderation, focusing on metrics design, evaluation, monitoring, and safe rollout for a multi-label audio violation detector within the Analytics & Experimentation domain.
Roblox
Jul 31, 2025, 12:00 AM
Software Engineer
Onsite
Analytics & Experimentation
0
0
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:
- Product and ML metrics (precision/recall, per-class FP/FN rates, manual-review yield, inter-rater agreement, etc.).
- System/ops metrics (batch/streaming latency SLOs, throughput, queue depths, failure rates, cost per hour of audio).
- Alert thresholds and dashboards.
- Sampling and canary strategies for new models/thresholds.
- How to run A/B tests or shadow evaluations before full rollout.
Solution
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