Google Exam Professional Machine Learning Engineer Topic 3 Question 92 Discussion

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

Question #: 92
Topic #: 3

[All 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 Jenelle at Oct 22, 2024, 08:45 PM

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Tarra

10 days ago

Option C is the way to go, folks. Vertex AI Pipelines is the future of ML ops. It's like having a personal ML butler to handle all the tedious tasks for you. Just sit back and let the pipeline do its thing!

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Bo

11 days ago

Hmm, option D with the HyperParameterTuningJobRunOp component sounds interesting. Gotta love those hyperparameter tuning capabilities! But I think option C is the safest bet for a reliable and repeatable training process.

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Haydee

12 days ago

Option B looks good, but I think the Metadata API is a bit of a hassle. Why not just go with the full Vertex AI Pipelines solution in option C? It's like a one-stop-shop for your ML ops needs.

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Myra

5 days ago

Option B looks good, but I think the Metadata API is a bit of a hassle.

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Sheridan

19 days ago

I'm a big fan of Vertex AI, and option C seems to cover all the bases. Cloud Scheduler and Cloud Functions make it easy to schedule the pipeline runs. Plus, the Model Registry is a great way to manage your model lineage.

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Brandon

8 days ago

Option C sounds like a solid choice. Managed pipelines in Vertex AI Pipelines are great for automating the training process.

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Mi

26 days ago

Option C is the way to go! Vertex AI Pipelines is the perfect solution to automate your training process and keep track of your model artifacts. The ModelUploadOp component is a game-changer for version control.

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Cherri

14 days ago

Option C is the way to go! Vertex AI Pipelines is the perfect solution to automate your training process and keep track of your model artifacts.

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Shawna

2 months ago

I agree with Margot. Option A seems like a reliable way to operationalize the training process and ensure repeatable results.

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Margot

2 months ago

I think option A is the best choice. It allows us to train the model weekly and track versions using the Vertex AI SDK.

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