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 files are being copied to the SRC_dir folder?

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

Describe the functionality of the predictor.py file.

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

Explain the difference between container registry and artifact registry.

What is the significance of the build_CPR_model function?

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

What is the role of the local endpoint in the model deployment process?

What does the health check response indicate about the model?

What steps are involved in deploying a model to the model registry?