Data Science Model Deployments and Cloud Computing on GCP - Lab - Final Pipeline Visualization Using Vertex UI and Walkt

The video tutorial explains the successful execution of a pipeline with three stages, taking about 15 minutes. It covers the deployment process and the importance of selecting the correct region. The tutorial then delves into the pipeline root folder, detailing the contents of each step: data retrieval, training, and deployment. It highlights the presence of executor output files and other artifacts. The video concludes by introducing the concept of model evaluation, emphasizing its role in deciding whether to deploy a trained model, which will be explored in the next video.

How long did the pipeline execution take?

• 10 minutes

• 15 minutes

• 20 minutes

• 25 minutes

What does the status indicate after clicking on the endpoint?

• Ready

• Not Ready

• In Progress

• Failed

What are the three steps or components in the ML workflow mentioned?

• Data Collection, Training, Deployment

• Data Cleaning, Training, Evaluation

• Data Collection, Evaluation, Deployment

• Training, Testing, Deployment

What files can be found in the 'Get CC fraud data' directory?

• Test dataframe, Train dataframe, Executor output JSON file

• Model file, Output artifact, Executor output JSON file

• Data file, Configuration file, Log file

• None of the above

What is the significance of model evaluation in the workflow?

• It ensures the model is trained correctly

• It determines if the model should be deployed

• It is not necessary

• It only checks the data quality