Google Professional Machine Learning Engineer
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As a manager of a data science team utilizing a complex cloud-based backend for training jobs, you're facing management challenges due to the team's use of diverse frameworks like Keras, PyTorch, Theano, Scikit-learn, and custom libraries. The solution must not only streamline the process but also adhere to cost constraints and ensure scalability for future framework additions. Which managed service would best meet these requirements? Choose the best option.
A
Configure Kubeflow on Google Kubernetes Engine to handle training jobs via TFJob, ensuring compatibility with TensorFlow-based frameworks but potentially limiting support for others.
B
Utilize the AI Platform's custom containers feature to accept training jobs across any framework, including custom libraries, offering flexibility and reducing management overhead.
C
Establish a Slurm workload manager for scheduling and executing jobs on your cloud infrastructure, which requires significant setup and maintenance but offers fine-grained control.
D
Develop a library of VM images on Compute Engine, making them available in a centralized repository, which could increase costs and complexity due to the need for multiple images for different frameworks.
E
Implement a combination of AI Platform's custom containers for most frameworks and Kubeflow for TensorFlow-based jobs, ensuring broad compatibility and optimized performance where possible. Choose two correct options.