How should you reduce your Google Cloud compute costs without impacting the model’s performance?

You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually

takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team’s spending.

How should you reduce your Google Cloud compute costs without impacting the model’s performance?
A . Use AI Platform to run distributed training jobs with checkpoints.
B . Use AI Platform to run distributed training jobs without checkpoints.
C . Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
D . Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.

Answer: D