Data Science Model Deployments and Cloud Computing on GCP - Lab - Model Deployment Flow Using Python SDK

Data Science Model Deployments and Cloud Computing on GCP - Lab - Model Deployment Flow Using Python SDK

Assessment

Interactive Video

Information Technology (IT), Architecture, Social Studies

University

Hard

Created by

Wayground Content

FREE Resource

The video tutorial guides viewers through the process of preparing and uploading files to a workbench, explaining the predictor.py script, running a model deployment script, setting up an artifact registry, building and deploying a model locally, and finally deploying the model to a registry and endpoint using Google Cloud AI Platform. Key steps include creating a repository, building a custom prediction routine, and performing a health check to ensure successful execution.

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10 questions

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1.

OPEN ENDED QUESTION

3 mins • 1 pt

What files are being copied to the SRC_dir folder?

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2.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the purpose of the requirements.txt file mentioned in the text?

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3.

OPEN ENDED QUESTION

3 mins • 1 pt

Describe the functionality of the predictor.py file.

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4.

OPEN ENDED QUESTION

3 mins • 1 pt

What command is used to install dependencies from requirements.txt in Jupyter Notebook?

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5.

OPEN ENDED QUESTION

3 mins • 1 pt

Explain the difference between container registry and artifact registry.

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6.

OPEN ENDED QUESTION

3 mins • 1 pt

What is the significance of the build_CPR_model function?

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7.

OPEN ENDED QUESTION

3 mins • 1 pt

How do you create a JSON file for input data for the model?

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