Your company deploys models by hand. A data scientist trains a model, posts the metrics in Slack, someone eyeballs them, and an engineer copies the artifact to production during a deploy window. It takes about a week and it has gone wrong twice this year: once a model trained on a stale dataset shipped, and once a rollback took four hours because nobody was sure which artifact had been running.
You have 40 models, some retrained daily, some quarterly. Latency-critical models serve user traffic; others run in batch.
You want continuous deployment: a retrained model that passes automated gates should reach production without a human in the loop, and a bad one should be caught and rolled back automatically. Leadership is nervous about removing human approval, so the system must earn that trust with evidence.
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.
Why can you not simply gate on offline metrics the way software CI gates on tests?
A model's business metric takes 8 days to move. How do you promote it in an afternoon?
Rollback took four hours. What in your design makes it take four seconds?
Minimum 8 components · needs a wide desktop screen