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?

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A. Use the AI Platform custom containers feature to receive training jobs using any framework.

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B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF

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C. Create a library of VM images on Compute Engine, and publish these images on a centralized

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D. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud

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…

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