Design Meta Ads Insights Ranker

Product Context

Meta Ads Manager and Ads Insights are used by advertisers, account managers, and internal ops teams to monitor campaign delivery. Design an ML system for a real-time dashboard that tracks ad impressions, highlights important changes, and ranks which campaigns or ad sets need attention first.

Scale

Task

1. Define the product requirements for a dashboard that does more than show raw counts: it should prioritize, rank, and explain impression changes in real time.
2. Estimate system scale for ingestion, feature computation, model serving, and storage.
3. Propose an end-to-end ML architecture, including candidate generation, ranking, and optional re-ranking for the entities shown on the dashboard.
4. Choose models for each stage and explain how you would serve them online versus compute them in batch.
5. Define offline and online evaluation, including how you would validate alert quality, ranking quality, and business impact.
6. Identify failure modes such as delayed event pipelines, feature drift, training-serving skew, and bad anomaly spikes, and explain mitigation.

Constraints

• Impression counts must be queryable at campaign, ad set, and ad granularity.
• The dashboard must support both global views and personalized views per advertiser or account manager.
• Some labels are delayed: true business outcomes like spend efficiency or conversions may arrive hours later.
• Cost matters: most traffic should be served from pre-aggregated features and lightweight models, not expensive per-request deep inference.
• The system must degrade gracefully if streaming features are late or unavailable.