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title: "Comment by Ahmet Özel on \"ML Inference Inside the Bid Request: Feature Fetch, Batching, and the Fallback Price\" | Hashnode"
description: "The separate fetch and evaluation budgets make the fallback ladder measurable instead of a hidden latency shortcut. I would also slice calibration by fallback r"
url: "https://hashnode.com/posts/ml-inference-inside-the-bid-request-feature-fetch-batching-and-the-fallback-price/6ac389b847511d51288a62b2/comment/6ac39a5e076e470931f05af5"
---

[Discussion](https://hashnode.com/posts/ml-inference-inside-the-bid-request-feature-fetch-batching-and-the-fallback-price/6ac389b847511d51288a62b2)

Comment [ML Inference Inside the Bid Request: Feature Fetch, Batching, and the Fallback Price](https://hashnode.com/posts/ml-inference-inside-the-bid-request-feature-fetch-batching-and-the-fallback-price/6ac389b847511d51288a62b2)

[Ahmet Özel](https://hashnode.com/@ahmetozel)

[Oct 5](https://hashnode.com/posts/ml-inference-inside-the-bid-request-feature-fetch-batching-and-the-fallback-price/6ac389b847511d51288a62b2/comment/6ac39a5e076e470931f05af5)

The separate fetch and evaluation budgets make the fallback ladder measurable instead of a hidden latency shortcut. I would also slice calibration by fallback reason, because remote-feature timeouts may be concentrated in particular users, exchanges or traffic conditions rather than resemble random missing features.

Training with a randomly absent feature group could miss that selection effect. A useful shadow check would replay the observed timeout subset through the reduced model and compare its predicted rates with mature outcomes, while keeping the full-model result as a counterfactual only where those features actually arrived. That would connect your fallback-rate dashboard to the price-quality risk of the specific traffic falling down the ladder.