Data Science Model Deployments and Cloud Computing on GCP - Overview - New Use Case

This video provides an overview of a fraud detection use case using Google App Engine. It discusses the input data set of anonymized credit card transactions stored in Big Query, with 30 input attributes and one output variable indicating fraud. The model development uses the Random Forest algorithm with Scikit-Learn, and the deployment is on Google App Engine's flexible environment. Key steps include data validation, model training, and serving predictions.

What is the primary focus of the machine learning use case discussed in the video?

• Image recognition

• Fraud detection

• Natural language processing

• Weather prediction

Which algorithm is used for model development in this use case?

• K-Nearest Neighbors

• Support Vector Machine

• Neural Networks

• Random Forest

On which platform will the model be deployed?

• Amazon Web Services

• Microsoft Azure

• Google App Engine

• IBM Cloud

What is the first step in handling the fraud detection use case?

• Data visualization

• Model deployment

• Model training

• Input data validation

What is the final step in the process outlined for the use case?

• Model serving and predictions

• Algorithm selection

• Data collection

• Feature engineering