Google
PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #8
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using
The correct answer is A. Use AI Platform for distributed training.. AI platform provides lower infrastructure overhead and allows you to not have to refactor your code too much (no containerization and such, like in KubeFlow).
Submitted by sofia.br· Apr 18, 2026ML model development
Question
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?
Options
- AUse AI Platform for distributed training.
- BCreate a cluster on Dataproc for training.
- CCreate a Managed Instance Group with autoscaling.
- DUse Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
How the community answered
(39 responses)- A77% (30)
- B8% (3)
- C13% (5)
- D3% (1)
Explanation
AI platform provides lower infrastructure overhead and allows you to not have to refactor your code too much (no containerization and such, like in KubeFlow).
Topics
#Managed ML Services#Distributed Training#Cloud Migration#TensorFlow Estimators
Community Discussion
No community discussion yet for this question.