Data Science Model Deployments and Cloud Computing on GCP - Lab - Deploy Python Application to Cloud Run

This video tutorial covers deploying a Python Flask application to Cloud Run using two methods: Docker image and direct source code deployment. It explains the setup of the application, including the use of BigQuery, and details the deployment commands and configurations. The video also highlights the differences between the two deployment approaches and provides a step-by-step guide to deploying the application on Google Cloud Platform.

What is Cloud Run primarily used for?

• Managing databases

• Hosting static websites

• Running containerized applications

• Running virtual machines

Which two approaches can be used to deploy applications to Cloud Run?

• Deploying from GitHub and Bitbucket

• Creating a Docker image and deploying from source code

• Using virtual machines and serverless functions

• Using Kubernetes and Docker

What does the Python application in the video do?

• Calculates mathematical operations

• Queries a BigQuery table for post data

• Sends emails to users

• Generates random numbers

Which library is NOT mentioned as a requirement for the Python application?

• Gunicorn

• Flask

• Pandas

• NumPy

What is the base image used in the Dockerfile?

• Python 2.7

• Python 3.8-slim

• Ubuntu

• Node.js

What is the first step in deploying the application using the Docker approach?

• Creating a Kubernetes cluster

• Running the application locally

• Building the Docker image

• Pushing the image to GCR

What command is used to deploy the Docker image to Cloud Run?

• gcloud run deploy

• docker push

• kubectl apply

• gcloud app deploy

What is a key difference between the two deployment approaches discussed?

• The first approach is faster

• The second approach abstracts the containerization process

• The first approach does not use containers

• The second approach requires more manual steps

What is the maximum number of instances allowed in the deployment settings?

• 10

• 1

• 5

• 2

Why does the second deployment approach take longer?

• It involves more manual configuration

• It builds the container during deployment

• It uses a slower server

• It requires more network bandwidth