Data Science Model Deployments and Cloud Computing on GCP - Lab - Run and Ship Applications Using the Container Registry

This tutorial covers the process of developing a machine learning model, containerizing it using Docker, and deploying it to Google Cloud's Container Registry. It includes steps for setting up the environment, building and running Docker containers, and sharing the Docker images with team members. The tutorial also explains how to execute the model locally and retrieve output metrics.

What is the primary goal of this lecture?

• To explore different machine learning algorithms

• To set up a local server environment

• To understand how to containerize a machine learning model

• To learn how to develop a web application

What is the task of the machine learning model discussed in the lecture?

• To identify fraudulent transactions

• To predict stock prices

• To recommend products

• To classify images

Which base image is used in the Dockerfile for this application?

• Node.js 14

• Ubuntu 20.04

• Python 3.10 slim

• Python 3.8

What Python library is used for data manipulation in the script?

• NumPy

• Matplotlib

• Pandas

• TensorFlow

What is the output format of the classification report generated by the model?

• CSV

• XML

• JSON

• TXT

What command is used to build the Docker image?

• docker assemble

• docker compile

• docker build

• docker create

What does the 'V' flag in the docker run command signify?

• Volume

• Version

• Verbose

• Virtual

Where is the Docker image pushed after being built?

• Azure Blob Storage

• Google Container Registry

• AWS S3

• Local File System

What is the purpose of tagging a Docker image?

• To add metadata

• To identify the image version

• To encrypt the image

• To compress the image

How can a team member pull the Docker image from the Google Container Registry?

• Using docker fetch

• Using docker pull

• Using docker retrieve

• Using docker get