You built a custom ML model using scikit-learn. Training time is taking longer than expected. You decide to migrate your model to Vertex AI Training, and you want to improve the model's training time.

Question

Options

• AMigrate your model to TensorFlow, and train it using Vertex AI Training.
• BTrain your model in a distributed mode using multiple Compute Engine VMs.
• CTrain your model with DLVM images on Vertex AI, and ensure that your code utilizes NumPy and
• DTrain your model using Vertex AI Training with GPUs.

• A
  6% (3)
• B
  4% (2)
• C
  80% (43)
• D
  11% (6)

Explanation

DLVM (Deep Learning VM) images on Vertex AI come pre-installed with hardware-optimized numerical libraries such as Intel MKL-accelerated NumPy and SciPy. Since scikit-learn heavily relies on NumPy under the hood, running it on a DLVM ensures those operations use optimized BLAS/LAPACK routines - providing a meaningful speedup with minimal code changes. This should be tried first because it is the least invasive option. scikit-learn has no native GPU support, so adding GPUs (D) would require significant refactoring. Migrating to TensorFlow (A) is a major rewrite. Distributed training across multiple VMs (B) requires rearchitecting the training loop.

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