Your platform serves 4,000 features to 60 models. Features come from batch jobs, streaming pipelines and request-time computation.
Three recurring failure classes hurt you. First, staleness: a batch job fails silently and models serve yesterday's, or last week's. Values. Second, training-serving skew: the offline and online values of the same feature disagree, so models trained on one thing predict on another. Third, silent upstream changes: a source team changes a unit from cents to dollars, or starts sending a new category value, and nothing breaks loudly.
Models degrade quietly in all three cases. You need to catch feature problems before models are affected, and to tell an engineer which of the 4,000 features caused it.
Build the architecture on a canvas: place the components, configure them, connect them into a data flow, and write a short reason for each one. The AI reviewer grades your design against a rubric written specifically for this problem.
A batch job fails and produces no output at all. Why would a naive monitor see nothing wrong?
An upstream team changes a field from cents to dollars. Which of your checks fires, and which ones pass?
How do you actually measure training-serving skew rather than inferring it from a model getting worse?
Minimum 6 components · needs a wide desktop screen