Google Cloud Professional Machine Learning Engineer PMLE — Topic 1 Question 70

Question

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?

Accepted Answer

Correct answer: C. The correct answer is C. Option C: Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints. Google Kubernetes Engine runs managed Kubernetes for containerized workloads. Preemptible/Spot VMs use spare capacity at a large discount for fault-tolerant, interruptible work.

Google Cloud Professional Machine Learning Engineer PMLE — Question 70