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Question 53 - Professional Machine Learning Engineer discussion

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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?

A.
Use Vertex Al Platform for distributed training
Answers
A.
Use Vertex Al Platform for distributed training
B.
Create a cluster on Dataproc for training
Answers
B.
Create a cluster on Dataproc for training
C.
Create a Managed Instance Group with autoscaling
Answers
C.
Create a Managed Instance Group with autoscaling
D.
Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
Answers
D.
Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.
Suggested answer: A

Explanation:

Vertex AI Platform is a unified platform for building and deploying ML models on Google Cloud. It supports both custom and AutoML models, and provides various tools and services for ML development, such as Vertex Pipelines, Vertex Vizier, Vertex Explainable AI, and Vertex Feature Store. Vertex AI Platform allows users to train their TensorFlow models using distributed training, which can speed up the training process and handle large datasets. Vertex AI Platform also minimizes code refactoring and infrastructure overhead, as it is compatible with TensorFlow Estimators and handles the provisioning, configuration, and scaling of the training resources automatically. The other options are not as suitable for this scenario. Dataproc is a service that allows users to create and run data processing pipelines using Apache Spark and Hadoop, but it is not designed for TensorFlow model training. Managed Instance Groups are a feature that allows users to create and manage groups of identical compute instances, but they require more configuration and management than Vertex AI Platform. Kubeflow Pipelines are a tool that allows users to create and run ML workflows on Google Kubernetes Engine, but they involve more complexity and code changes than Vertex AI Platform.Reference:

Vertex AI Platform documentation

Distributed training with Vertex AI Platform

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asked 18/09/2024
Oleksandr Kondratchuk
35 questions
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