Data Science Model Deployments and Cloud Computing on GCP - Lab - Model Serving Using Endpoint with Python SDK

The video tutorial covers deploying an endpoint in Vertex AI, verifying its status, and running predictions. It explains optional deployment parameters like traffic splitting and replica counts. A function for batch predictions using Python is demonstrated. The tutorial concludes with a summary and an assignment for further practice.

What steps should you take to check if your endpoint is active after deployment?

Explain how you can run predictions against your deployed model.

What parameters can you specify when deploying a model?

How can you split traffic between different versions of the same model?

What considerations should you make regarding replica counts for your model?

Describe the function you would use to run batch predictions.

What is the significance of the project ID and endpoint ID in the prediction process?