Data Science Model Deployments and Cloud Computing on GCP - Lab - Custom Model Training Using SDK and Model Registries

Data Science Model Deployments and Cloud Computing on GCP - Lab - Custom Model Training Using SDK and Model Registries

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

University

Hard

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Quizizz Content

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The video tutorial demonstrates how to complete a manual training job using Vertex AI's web console and then replicate the process using Python's SDK. It covers setting up the Python SDK, Docker image, and service account, followed by executing a custom training job. Finally, it explains how to upload the trained model to Vertex AI's model registry, highlighting the importance of versioning and labeling models.

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7 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the purpose of the folder created in the GCS bucket?

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

Describe the process of submitting a manual training job using the Vertex AI web console.

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3.

OPEN ENDED QUESTION

3 mins • 1 pt

What steps are involved in running a custom training job using Python's SDK?

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4.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the significance of the service account in the training job process?

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5.

OPEN ENDED QUESTION

3 mins • 1 pt

Explain the parameters required for the custom container training job function.

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6.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the role of the model registry in Vertex AI?

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7.

OPEN ENDED QUESTION

3 mins • 1 pt

How do you upload a model to the Vertex AI model registry?

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