Spent the first few minutes thinking about routing and coverage, which felt right, but I underweighted the data pipeline side.
Start by clarifying the product goals and constraints, then break the problem into major components: data acquisition, processing, storage, and serving. Discuss trade-offs between coverage, quality, cost, and latency, and propose a phased rollout with metrics for success.
Pro tip: Emphasize the importance of prioritization: not all addresses are equally valuable, so focus on high-density areas first and use a tiered approach to balance cost and coverage. Also, mention the need for a feedback loop to continuously improve data quality.
Ask questions to understand the scope: What resolution? How often updated? What's the budget? Are there privacy concerns? This ensures alignment with product goals.
Plan how to deploy 1,000 cars efficiently: route optimization, scheduling, sensor setup (cameras, GPS, LiDAR), and data upload mechanisms. Consider partnerships or crowdsourcing for hard-to-reach areas.
Outline steps for ingesting, processing (stitching images, blurring faces/license plates), and storing data at scale. Discuss use of cloud services, batch vs. stream processing, and quality checks.
Define how users will access the imagery (APIs, web app) and how to serve low-latency queries. Consider caching, CDN, and indexing by address/geolocation.
Establish success metrics (coverage %, freshness, user engagement) and a feedback loop for continuous improvement. Plan for scaling and maintenance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.