Data Science Model Deployments and Cloud Computing on GCP - Overview - Problem Statement

The video tutorial provides an overview of developing a machine learning model to predict ad clicks using logistic regression. It covers the input data attributes, sourced from Kaggle, and explains the solution approach, including data reading, model training, and deployment on Google Cloud. The tutorial also discusses the model serving process, including endpoint creation and logging steps.

What is the main task described in the video?

• Develop a model to predict product sales

• Develop a machine learning model to predict ad clicks

• Create a website for advertisements

• Analyze social media trends

Which of the following is NOT an input attribute in the dataset?

• User's favorite color

• Daily time spent on the website

• Total income of the area

• Age of the person

What is the output variable in the dataset?

• Advertisement topic

• Daily internet usage

• Clicked on ad

• User's age

Which machine learning technique is used for training the model?

• Decision Trees

• Neural Networks

• Support Vector Machines

• Logistic Regression

What is the purpose of the model serving or inference layer?

• To collect more data

• To serve predictions by accepting input parameters

• To visualize data

• To clean the dataset