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Your team of data scientists is utilizing a cloud-based backend system for submitting training jobs, which has become increasingly complex and time-consuming to manage due to the variety of machine learning frameworks in use, including Keras, PyTorch, Theano, Scikit-learn, and custom libraries. You are tasked with selecting a managed service that simplifies administration while accommodating the diverse frameworks. The solution must also consider scalability, cost-effectiveness, and ease of integration with existing workflows. Which of the following options provides the best course of action? (Choose one correct option)
A
Configure Kubeflow to operate on Google Kubernetes Engine and accept training jobs via TF Job, leveraging its compatibility with TensorFlow for streamlined operations.
B
Establish a Slurm workload manager to handle and schedule jobs on your cloud infrastructure, offering fine-grained control over resource allocation and job scheduling.
C
Utilize the AI Platform custom containers feature to accommodate training jobs across any framework, providing the flexibility to use custom containers for any machine learning framework not natively supported.
D
Develop a collection of VM images on Compute Engine and share these images in a centralized repository, enabling data scientists to deploy their preferred environments quickly.