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

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.

What is the purpose of the requirements.txt file in the setup process?

• To define the directory structure

• To list the Python scripts to be executed

• To specify the dependencies needed for the project

• To provide a description of the project

Which Python module is used for building APIs as mentioned in the video?

• FastAPI

• Django

• Flask

• Tornado

What is the role of the 'load' function in the custom class defined in predictor.py?

• To load the model or artifact from a URI

• To save the model to a file

• To preprocess input data

• To visualize the model predictions

What is the significance of the 'region' variable in the setup?

• It determines the type of machine used

• It defines the size of the model

• It sets the language for the API

• It specifies the geographical location for deployment

What is the difference between a container registry and an artifact registry?

• Container registries are more secure than artifact registries

• Artifact registries are faster than container registries

• Artifact registries are only used for Java builds

• Container registries host only containers, while artifact registries can host any artifact including containers

What does the build_CPR_model function do?

• It compiles the Python scripts

• It generates a report of model accuracy

• It builds a custom prediction routine using a local endpoint

• It deploys the model to the cloud

What is the purpose of the instances.json file?

• To store the model's training data

• To define the model's configuration settings

• To provide sample input data for predictions

• To log the model's prediction results

What does a response code of 200 indicate in the health check?

• The model is not yet deployed

• The model has failed the health check

• The model passed the health check successfully

• The model is in the process of being deployed

What is the final step before running predictions against the deployed model?

• Creating a new Jupyter notebook

• Uploading the model to the model registry

• Running a health check

• Building a Docker image

How long does it typically take to deploy a model from the model registry to the endpoint?

• 20-25 minutes

• 12-15 minutes

• 5-7 minutes

• 1-2 minutes