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You are managing a team of data scientists who are currently using a cloud-based backend system to submit their machine learning training jobs. Due to the increasing complexity and difficulty in administering this system, you are considering transitioning to a managed service. The team uses multiple frameworks for their work, including Keras, PyTorch, Theano, scikit-learn, and custom libraries. You need a solution that supports these diverse frameworks and simplifies the job submission process. What should you do?
A
Use the Vertex AI Training to submit training jobs using any framework.
B
Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.
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.