Exam Professional Machine Learning Engineer topic 1 question 162 discussion

Scikit-learn is not intended to be used as a deep-learning framework and it does not provide any GPU support. (Ref: https://stackoverflow.com/questions/41567895/will-scikit-learn-utilize-gpu). So I go with B

You decided to migrate to Vertex AI, If you have a model that requires significant computational resources and doesn't rely heavily on specialized GPU operations (like those in option D), then option B might still be a good choice. However, if your model is computationally intensive or involves complex neural network architectures I would go with D instead of B.

B is correct, because scikit only has CPU support for the following services: - prebuilt containers for custom training (this is the case here) - prebuilt containers for predictions and explanations - Vertex AI Pipelines - Vertex AI Workbench user-managed notebooks https://cloud.google.com/vertex-ai/docs/supported-frameworks-list#scikit-learn_2

scikit-learn no GPU support.

Scikit-learn doesn't natively support GPUs for training. However, many scikit-learn algorithms rely on libraries like NumPy and SciPy. These libraries can leverage GPUs if they're available on the system, potentially benefiting scikit-learn models indirectly.

SK-Learn offers no GPU support. Answer is B!

GPU helps speeding up training process

Why no A?

B. Train your model using Vertex AI Training with CPUs. No GPUs for ScikitLearn, but parrallelize/distribute training is a good way to increase model building