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Google Exam Professional Machine Learning Engineer Topic 6 Question 79 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam
Question #: 79
Topic #: 6
[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?

Show Suggested Answer Hide 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]


Contribute your Thoughts:

Danica
5 months 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
4 months 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
5 months 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
5 months 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
5 months ago
I agree with Merissa, using the Vertex AI SDK and Notebooks API seems like a reliable approach.
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Tamera
5 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
5 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
5 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
5 months 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
5 months 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
5 months 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
6 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.
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Chauncey
5 months ago
I agree, using Vertex AI Pipelines, Model Registry, and Cloud Scheduler/Functions seems like a solid plan.
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Christene
5 months ago
I think Option C is the best choice for operationalizing the training process.
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Annmarie
5 months ago
Yes, using Vertex AI Pipelines, Model Registry, and Cloud Scheduler/Functions together will definitely streamline the training process.
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Dawne
5 months ago
I agree, option C seems like the best choice for setting up a reliable and repeatable model training process.
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Merissa
6 months ago
I think option A sounds like a good way to operationalize the training process.
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