Topic 1 Question 70

Professional Machine Learning Engineer

Topic 1 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?
- Use AI Platform to run distributed training jobs with checkpoints.
- Use AI Platform to run distributed training jobs without checkpoints.
- Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
- Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.
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コメント(12)
- 正解だと思う選択肢: C
  https://cloud.google.com/blog/products/ai-machine-learning/reduce-the-costs-of-ml-workflows-with-preemptible-vms-and-gpus?hl=en
  
  👍 7
  seifou2022/12/16
- 正解だと思う選択肢: C
  C - Reduce cost with preemptive instances and add checkpoints to snapshot intermediate results
  
  👍 3
  neochaotic2022/12/10
- "A Preemptible VM (PVM) is a Google Compute Engine (GCE) virtual machine (VM) instance that can be purchased for a steep discount as long as the customer accepts that the instance will terminate after 24 hours." This excludes C and D. Checkpoints are needed for long processing, so A.
  
  👍 3
  ares812022/12/11
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