Data Science Model Deployments and Cloud Computing on GCP - Lab - Model Endpoint Deployment

The video tutorial provides a recap of previous lessons on mole training using the web console and Python SDK. It introduces the process of uploading models to a Vertex AI registry and deploying them to an endpoint for online and batch predictions. The tutorial explains the folder structure, including key files like predictor.py and requirements.txt. It outlines the steps for training a model, deploying it as a Docker image to a local endpoint, and finally uploading it to a Vertex AI model registry for deployment. The video concludes with running predictions against the deployed model.

What is the purpose of uploading a model to the Vertex AI model registry?

• To delete the model

• To serve predictions online and in batch

• To change the model's format

• To train the model again

What is the main function of the 'predictor.py' script in the folder structure?

• To train the model

• To load the model and serve predictions

• To delete the model

• To upload the model to the cloud

Which file contains the list of libraries needed at runtime?

• model_deploy_endpoint.py

• model_artifact

• requirements.txt

• predictor.py

What is the first step in the model deployment process?

• Uploading the model to a GCS bucket

• Running predictions

• Training the model

• Deploying the model to a Vertex AI endpoint

What is the final step in the model deployment process?

• Training the model

• Uploading the model to a GCS bucket

• Running predictions against the endpoint

• Building a Docker image