You built a custom Vertex AI pipeline job that preprocesses images and trains an object...

To reduce training time without sacrificing accuracy, you must scale your hardware resources rather than reducing the complexity of the model or the size of the data.

Vertical and Horizontal Scaling: In Vertex AI Custom Training, the WorkerPoolSpec allows you to define the hardware for your cluster. By increasing the number of GPUs (from 1 to 3) and CPUs (from 8 to 24), you enable faster parallel processing of image data and faster gradient updates during backpropagation.

Why other options are incorrect:

Options A and B: Reducing layers or using a subset of the data directly compromises the model ' s ability to learn complex patterns, leading to lower accuracy.

Option C: Increasing only the vCPUs might help if your bottleneck is image preprocessing (CPU-bound), but for object detection, the training bottleneck is almost always the GPU. Adding more GPUs (as in Option D) provides the most significant reduction in training time.