Data Science Model Deployments and Cloud Computing on GCP - Overview – Vertex AI UI

The video provides a quick tour of Vertex AI's user interface, highlighting its main components and recent changes. It covers the dashboard, workbench for Jupyter notebooks, pipelines for machine learning orchestration, and model development sections like training and model registry. The video also explains endpoints for deploying models and managing predictions, both batch and online.

What is the first step to access Vertex AI in your GCP account?

• Click on the dashboard

• Type Vertex AI in the search bar

• Open the workbench

• Navigate to the pipelines section

Which component of Vertex AI allows you to quickly set up Jupyter notebooks?

• Model Registry

• Pipelines

• Workbench

• Dashboard

What is the primary use of the pipelines component in Vertex AI?

• To deploy models

• For machine learning orchestration using Cube Flow

• To manage endpoints

• For batch predictions

Where are different versions of machine learning models deployed in Vertex AI?

• Dashboard

• Model Registry

• Workbench

• Endpoints

What is the function of endpoints in Vertex AI?

• To create Jupyter notebooks

• To serve predictions for batch and online

• To train models

• To orchestrate machine learning pipelines