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Question 266 - Professional Machine Learning Engineer discussion

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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?

A.
1. 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.
Answers
A.
1. 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.
B.
1. 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.
Answers
B.
1. 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.
C.
1. 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.
Answers
C.
1. 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.
D.
1. 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.
Answers
D.
1. 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.
Suggested answer: C

Explanation:

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]

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asked 18/09/2024
Mark Anthony Simon
38 questions
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