Product Context

ShopNow is a large omnichannel retailer with a marketplace and first-party inventory across thousands of stores. The pricing platform must update item prices in near real time as demand, competitor prices, inventory, and local store conditions change.

Scale

Task

Design an end-to-end ML system that decides and serves real-time prices for millions of items across thousands of stores.

1. Clarify the product objective and define the optimization target: revenue, margin, sell-through, conversion, or a constrained combination.
2. Propose the full architecture, including event ingestion, feature computation, candidate price generation, ranking/scoring, policy constraints, and online serving.
3. Choose models for each stage and explain what runs in batch vs near-real-time vs per-request online inference.
4. Define the training data, labels, feedback loop, and how you handle delayed outcomes such as purchases and returns.
5. Specify offline evaluation, online experimentation, and monitoring for drift, training-serving skew, and policy violations.
6. Identify key failure modes and fallback behavior when data feeds, models, or serving systems are degraded.

Constraints

• Prices must respect merchant rules: floor/ceiling bounds, MAP restrictions, promo calendars, and regional tax differences.
• Inventory, competitor prices, and demand signals can change within minutes.
• The system must support both store-specific and global prices.
• Bad pricing decisions are high-risk: legal/compliance violations and margin loss must be tightly controlled.
• Serving cost matters: most traffic should be handled on CPU with selective use of more expensive models.