Google Exam Professional-Machine-Learning-Engineer Topic 6 Question 79 Discussion

Actual exam question for Google's Google Professional Machine Learning Engineer exam

Question #: 79
Topic #: 6

[All Google Professional Machine Learning Engineer Questions]

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

A1. Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model.
2. Using the Notebooks API, create a scheduled execution to run the training code weekly.

B1. Create an instance of the CustomJob class with the Vertex AI SDK to train your model.
2. Use the Metadata API to register your model as a model artifact.
3. Using the Notebooks API, create a scheduled execution to run the training code weekly.

C1. Create a managed pipeline in Vertex Al Pipelines to train your model by using a Vertex Al CustomTrainingJoOp component.
2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry.
3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

D1. Create a managed pipeline in Vertex Al Pipelines to train your model using a Vertex Al HyperParameterTuningJobRunOp component.
2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry.
3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

Show Suggested Answer

Suggested Answer: C

The best way to operationalize your training process is to use Vertex AI Pipelines, which allows you to create and run scalable, portable, and reproducible workflows for your ML models. Vertex AI Pipelines also integrates with Vertex AI Metadata, which tracks the provenance, lineage, and artifacts of your ML models. By using a Vertex AI CustomTrainingJobOp component, you can train your model using the same code as in your Jupyter notebook. By using a ModelUploadOp component, you can upload your trained model to Vertex AI Model Registry, which manages the versions and endpoints of your models. By using Cloud Scheduler and Cloud Functions, you can trigger your Vertex AI pipeline to run weekly, according to your plan.Reference:

Vertex AI Pipelines documentation

Vertex AI Metadata documentation

Vertex AI CustomTrainingJobOp documentation

ModelUploadOp documentation

Cloud Scheduler documentation

[Cloud Functions documentation]

by Fletcher at May 29, 2024, 10:16 AM

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Danica

25 days ago

Seriously, who comes up with these names? 'CustomTrainingJob', 'CustomJob', 'HyperParameterTuningJobRunOp' - it's like a game of ML-themed Mad Libs!

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Jenise

1 days ago

C) Create a managed pipeline in Vertex AI Pipelines to train your model by using a Vertex AI CustomTrainingJob component. Use the ModelUploadOp component to upload your model to Vertex AI Model Registry. Use Cloud Scheduler and Cloud Functions to run the Vertex AI pipeline weekly.

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Gail

6 days ago

B) Create an instance of the CustomJob class with the Vertex AI SDK to train your model. Use the Metadata API to register your model as a model artifact. Using the Notebooks API, create a scheduled execution to run the training code weekly.

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Glendora

16 days ago

A) Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model. Using the Notebooks API, create a scheduled execution to run the training code weekly.

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Francisca

1 months ago

I agree with Merissa, using the Vertex AI SDK and Notebooks API seems like a reliable approach.

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Tamera

1 months ago

Hmm, I'm not sure I understand the difference between the CustomTrainingJob and CustomJob classes in Vertex AI. Option A and B seem similar, but C looks more comprehensive.

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Cyril

1 months ago

I'll go with option C. It has all the bells and whistles, like the Model Registry and weekly scheduling. Plus, it's got a cool name - 'Vertex AI Pipelines'. It's like a superhero team for your ML model!

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Lindsey

1 months ago

I'm torn between options B and C. Both seem to address the key requirements, but C seems to provide a more managed and scalable solution with the Vertex AI Pipelines.

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Lashawnda

9 days ago

You should go with Option C for a more scalable solution. It seems to align better with your requirements.

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Vivienne

14 days ago

I think Option C might be more efficient. It involves using Vertex AI Pipelines for a managed training process.

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Vanda

18 days ago

Option B could be a good choice for you. It covers registering your model as an artifact and scheduling weekly training.

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Miriam

1 months ago

Option C looks the most comprehensive and efficient approach to operationalize the training process. Integrating Vertex AI Pipelines, Model Registry, and Cloud Scheduler/Functions seems like the right way to go.

upvoted 0 times