Exam Professional Machine Learning Engineer topic 1 question 162 discussion

Scikit-learn generally relies on CPU-based computations and does not natively leverage GPUs for most algorithms. Answer A is the best first step to improve training time without sacrificing model performance

Minimal changes – You can quickly migrate your existing scikit-learn code to Vertex AI Training using CPU instances. ✅ Vertex AI prebuilt containers already support scikit-learn with CPU (no extra setup needed). ✅ Lower cost than distributed training or switching to another framework. ✅ Good for establishing a baseline – Once you see how long it takes on Vertex AI, you can decide if further optimization (like distributed training) is needed.

B) The statement asks the FIRST STEP to take. Considering: - Scikit-learn's limited and non-universal GPU support - Higher cost associated with GPU instances The first sensible approach would indeed be to first migrate the model to Vertex AI using CPUs to establish a baseline training time. This allows for a direct comparison with the existing training setup and helps determine if the improvement from CPU to GPU is necessary.

I think it is D. The optimal approach to improve training time in Vertex AI Training is to leverage the parallel processing power of GPUs.

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