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Question 252 - Professional Machine Learning Engineer discussion

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You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?

A.
Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.
Answers
A.
Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.
B.
Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs
Answers
B.
Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs
C.
Deploy a matrix factorization model training job by using BigQuery ML.
Answers
C.
Deploy a matrix factorization model training job by using BigQuery ML.
D.
Deploy the training jobs by using Compute Engine instances with A100 GPUs and use the t f. nn. embedding_lookup API.
Answers
D.
Deploy the training jobs by using Compute Engine instances with A100 GPUs and use the t f. nn. embedding_lookup API.
Suggested answer: A

Explanation:

TPU VMs with TPUv3 Pod slices are the most scalable and performant option for training large-scale recommender models on Google Cloud. TPUv3 Pods can provide up to 2048 cores and 32 TB of memory, and can process billions of examples and features in minutes. The TPUEmbedding API is designed to efficiently handle large-scale categorical features and embeddings, and can reduce the memory footprint and communication overhead of the model. The other options are either less scalable (B and C) or less efficient (D) for this use case.

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asked 18/09/2024
lawrence Ajibolade
49 questions
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