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Google Professional Machine Learning Engineer Exam - Topic 11 Question 9 Discussion

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-team, and custom libraries. What should you do?
D) Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
A) Use the Al 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 TFJob
C) Create a library of VM images on Compute Engine; and publish these images on a centralized repository

Google Professional Machine Learning Engineer Exam - Topic 11 Question 9 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 9
Topic #: 11
[All Professional Machine Learning Engineer Questions]

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-team, and custom libraries. What should you do?

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Suggested Answer: D

by Gregoria at May 03, 2022, 06:02 AM

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Ronald
7 months ago
A is definitely the way to go for custom setups!
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Aileen
8 months ago
C is just too much work for managing images.
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Janae
8 months ago
Wait, can Slurm really handle all those frameworks?
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Lelia
8 months ago
I disagree, B could be more efficient with Kubernetes.
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Hyun
8 months ago
A seems like the best option for flexibility with frameworks.
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Lanie
8 months ago
Setting up Slurm seems like overkill for this scenario. I feel like a managed service would be a better fit, but I can't recall the specifics.
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Stacey
8 months ago
I practiced a similar question about using custom containers before. I think option A is the best choice for flexibility with different frameworks.
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Gladys
8 months ago
I'm not entirely sure, but I think Kubeflow is more tailored for TensorFlow. Would it really handle all those other frameworks well?
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Lizbeth
8 months ago
I remember reading about managed services and how they can simplify administration. Option A sounds like it could work since it supports multiple frameworks.
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Judy
8 months ago
Invoicing seems like the most logical option to me. That's where you'd handle the actual billing and splitting of costs.
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Huey
9 months ago
Hmm, this looks like it's asking about the WebLogic Admin Console and where to find information on the Store and Forward components. I think the key is to focus on the specific components mentioned in the question.
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