Question 5

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?

A. Use the AI Platform custom containers feature to receive training jobs using any framework.
B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
C. Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

References

https://cloud.google.com/ai-platform/training/docs/train-ml-model-pytorch
https://cloud.google.com/ai-platform/prediction/docs/use-custom-container
https://cloud.google.com/ai-platform/training/docs/containers-overview#advantages_of_custom_containers

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