
Ultimate access to all questions.
You manage a team of data scientists who frequently submit training jobs to a cloud-based backend system. Over time, managing this system has become challenging and cumbersome. You are considering switching to a managed service to simplify administration. The team utilizes a variety of machine learning frameworks, including Keras, PyTorch, Theano, Scikit-learn, and some custom libraries. Given these requirements and the need for a more manageable solution, 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.