You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom traini

Increasing instance memory or batch size might help if the current setup is memory-bound or batch size is too small, but it's not a direct solution for generically long training times and might impact performance if not tuned correctly.

Replacing a GPU with a TPU is a significant hardware and potential code change that, while possibly increasing performance, is a more complex and costly solution than simply enabling an existing training job option.

Early stopping monitors a model's performance on a validation set and stops training once the performance no longer improves (or begins to degrade) for a certain number of epochs. This prevents overfitting and significantly reduces total training time by avoiding unproductive computations without sacrificing final model quality.

Using `tf.distribute.Strategy` for distributed training would decrease training time but requires modifying the training code, which adds complexity and is not as simple as enabling an option within the Vertex AI Training job configuration.