Data Science Model Deployments and Cloud Computing on GCP - Lab - Workflow Template Introduction

The video tutorial introduces fraud detection and input validation, followed by an overview of GCP Workflows, a platform for process automation. It explains how to define workflow steps using YAML, focusing on data validation and model training. The tutorial details the use of HTTP GET and POST requests within workflows and demonstrates how to pass data between steps. The session concludes with a brief on the next focus area, model training.

What are the two output parameters discussed in the context of fraud detection?

• Is output valid and total attributes

• Is output correct and total values

• Is output valid and total values

• Is output correct and total attributes

What is GCP Workflows primarily used for?

• Security enhancement

• Data storage

• Network management

• Process automation and orchestration

In GCP Workflows, how can the order of services be defined?

• Using a SQL query

• Using a Python script

• Using a YAML or JSON file

• Using a Java program

What type of HTTP request is used for data validation in the workflow?

• PUT request

• POST request

• GET request

• DELETE request

How is the result from data validation used in model training?

• It is stored in a database

• It is used to generate a report

• It is ignored

• It is passed as input to model training