Question 70

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.

References

https://cloud.google.com/blog/products/ai-machine-learning/reduce-the-costs-of-ml-workflows-with-preemptible-vms-and-gpus?hl=en

https://www.kubeflow.org/docs/distributions/gke/pipelines/preemptible/

https://cloud.google.com/optimization/docs/guide/checkpointing

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Question 70