You need to deploy a scikit-leam classification model to production. The model must be able to serve requests 24/7, and you expect millions of requests per second to the production application from…

Setting the max replica count to 1 prevents auto-scaling and will cause the endpoint to fail under heavy load, unable to serve millions of requests per second.

Deploying an online Vertex AI prediction endpoint with a high max replica count (like 100) enables horizontal auto-scaling, which is essential to handle millions of requests per second during peak hours. Scikit-learn models are typically CPU-bound, so using CPUs by default (without specifying GPUs) minimizes cost while providing the necessary throughput through scaling.

Using GPUs for a scikit-learn model is generally unnecessary and significantly more expensive without providing proportional performance benefits, failing the cost minimization requirement; additionally, a single replica cannot handle the load.

While a high max replica count allows scaling, deploying with GPUs for a scikit-learn model is not cost-effective as these models typically do not benefit from GPU acceleration, thus violating the minimize cost requirement.