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-learn, and custom libraries. What should you do?
AI Platform custom containers feature allows teams to use a variety of frameworks including Keras, PyTorch, Theano, Scikit-learn, and custom libraries. This managed service provides the flexibility and scalability needed to handle different frameworks in a cloud-based backend system, making it easier to administer training jobs.
the answer is A
A, because AI platform supported all the frameworks mentioned. And Kubeflow is not managed service in GCP. https://cloud.google.com/ai-platform/training/docs/getting-started-pytorch
Now it's Vertex AI (instead of AI Platform), but it's the best solution, no need to do anything more complicated
A - Vertex AI now
A AI Platform should be correct You can build pipelines in Airflow, Kubeflow, Dataflow but they need to be managed over AI platform or Vertex AI.
the answer is A
A is correct
It's A
Went with A
A is the only Google managed service solution B,C - are not managed D- is a 3rd party
Correct answer is "A"
Here the question is on workload management not on supporting frameworks slurm is a managed solution for workloads
The Answer is D. As no where in the answer has GCP been mentioned. https://aadityachapagain.com/2020/09/distributed-training-with-slurm-on-gcp/
The answer must be D as nowhere in the question has GCP been mention https://aadityachapagain.com/2020/09/distributed-training-with-slurm-on-gcp/
D is incorrect, this is more far from a managed service based solution.
Chose A
The best approach is option A: Use AI Platform custom containers. It provides flexibility, scalability, and support for various frameworks, making it an ideal choice for your team’s needs.
A. Now it's called Vertex AI