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Sizing and Justifying the Catalog Precompute Pipeline
Part of the ML System Design Interview path →
Part of the Generative Vision & Image AI System Design path →
Finance asks you to justify the catalog-mode rendering pipeline's infrastructure cost, and separately, a teammate proposes an alternative: skip precomputing catalog images entirely, and instead generate the "garment on a model" image live, on-demand, the first time any shopper views that product page (caching the result after that first view).
- Using Step 1's catalog size and house-model-set assumptions, estimate the total number of renders the precompute pipeline needs to produce for the initial catalog, stating your assumptions explicitly.
- Evaluate the teammate's "generate on first view, cache after" proposal. What does it get right, and what real risk does it introduce that the batch-precompute design avoids?
- Is there a hybrid design that captures the teammate's intuition (don't waste compute on garments nobody views) without the risk you identified in part 2?
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