Topic 1 Question 5
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?
Use the AI Platform custom containers feature to receive training jobs using any framework.
Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.
Create a library of VM images on Compute Engine, and publish these images on a centralized repository.
Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
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the answer is A
👍 20gcp2021go2021/06/05A, 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
👍 7guruguru2021/07/23=New Question5= You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?
A. Use Principal Component Analysis to eliminate the least informative features. B. Use L 1 regularization to reduce the coefficients of uninformative features to 0. C. After building your model, use Shapley values to determine which features are the most informative. D. Use an iterative dropout technique to identify which features do not degrade the model when removed
👍 2MisterHairy2021/12/22
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