Design Agero Dispatch Decision Service

Product Context

Agero wants a real-time ML decision service for its roadside assistance dispatch workflow. When a driver requests help, the system should score and rank eligible service providers for that event so Agero can make a low-latency assignment recommendation to the dispatch operations surface.

Scale

Task

Design an end-to-end ML system on AWS using Python and SageMaker for this dispatch decision service. Your design should address:

1. How you would define the prediction target and success metrics for provider selection in Agero's dispatch flow
2. The full architecture for offline training and online inference, including feature computation, model deployment, and request routing
3. A multi-stage decision pipeline, including candidate retrieval/filtering, ranking, and any final policy or re-ranking layer
4. How you would support both real-time features (vehicle location, provider availability, weather, traffic) and batch features (historical acceptance rate, completion rate, average ETA error)
5. How you would evaluate the system offline and online, and how you would safely roll out model changes
6. The main failure modes, including feature drift, training-serving skew, stale provider state, and degraded downstream dependencies

Constraints

• The service must return a recommendation within 150ms p99 and remain available during regional surge events
• Provider availability and ETA-related features must be fresh within 1-2 minutes
• Some labels are delayed: completion outcomes and customer satisfaction may arrive hours later
• The system must be explainable enough for operations teams to understand why a provider was recommended
• Cost matters: avoid an architecture that requires GPUs for every online request unless clearly justified