Data Science Model Deployments and Cloud Computing on GCP - Lab - Code Walkthrough Using Kubeflow and Python

The video tutorial explains how to create and trigger a Cueflow pipeline on Vertex AI. It covers the installation of necessary modules, defining variables, and setting up the pipeline root. The tutorial details the steps for fetching data, training a model using Random Forest, and deploying the model on Vertex AI. It concludes with defining and compiling the pipeline using DSL.

What is the first step in setting up a Cueflow pipeline on Vertex AI?

• Creating a custom folder in GCS bucket

• Executing code blocks in the cloud

• Installing Python 3.9

• Opening the CC Fraud Model Cueflow notebook

What is the purpose of the 'pipeline root' variable in the Cueflow pipeline?

• To define the region for deployment

• To store the final model

• To specify the folder for output of each step

• To set the base image for containers

Which algorithm is used for training the model in the pipeline?

• K-Nearest Neighbors

• Support Vector Machine

• Linear Regression

• Random Forest Classifier

What is the output format of the trained model in the pipeline?

• CSV file

• Pickle file

• JSON file

• XML file

Which package is NOT required for deploying the model in the pipeline?

• Google Cloud AI Platform

• Psychic Learn

• Pandas

• Qflow

What is the purpose of the 'create endpoint' function in the deployment process?

• To fetch data from GCS

• To create an endpoint for the model

• To create a display name for the model

• To compile the pipeline

What is the final step in executing the pipeline?

• Uploading the model to a registry

• Defining the model name

• Running the start pipeline command

• Compiling the pipeline