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 libraries are required in the requirements.txt file for the application?

Explain how the model's predictions are evaluated using the classification report.

What steps are involved in pushing the Docker image to Google Container Registry?

How can a team member pull the Docker image from Google Container Registry to their local computer?