Product Context

Meta operates a large global backbone and datacenter network that supports Facebook, Instagram, WhatsApp, Messenger, and internal services. Design an ML-driven validation system that scores proposed network changes before rollout in NetNORAD-like change management workflows, so network engineers can catch risky changes early and reduce incidents.

Scale

Task

1. Clarify the product goal, prediction target, and what “validate before rollout” means operationally.
2. Design an end-to-end ML system, including candidate retrieval, ranking, and final re-ranking / policy gating for change approval.
3. Specify the offline and online data pipelines, labels, feature store design, and how you avoid training-serving skew.
4. Choose models for each stage and justify tradeoffs across accuracy, latency, interpretability, and cost.
5. Define offline evaluation, online rollout strategy, and guardrails for safe deployment.
6. Identify key failure modes, including feature drift, stale topology data, and false negatives that allow bad changes through.

Constraints

• The system must support both synchronous pre-submit validation and asynchronous deeper analysis after submission.
• Many labels are delayed: incidents may be attributed minutes to hours after a change, and attribution can be noisy.
• The final decision must remain human-auditable; engineers need explanations and top risk factors.
• Some regions have stricter compliance requirements, so raw configs may need redaction or feature hashing before model training.
• False negatives are much more expensive than false positives, but too many false positives will block engineer productivity and cause alert fatigue.