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.

What indicates that the pipeline has been successfully deployed?

• The root folder contains four directories.

• The pipeline takes less than 10 minutes.

• The endpoint status shows 'ready'.

• The model evaluation step is completed.

What is the name of the directory set as the pipeline root destination?

• ML Workflow

• CC fraud KFPL

• Data Pipeline

• Vertex

Which file is found in the 'train' directory of the pipeline root folder?

• Deployment script

• Test dataframe

• Executor output JSON

• Pickle model

What is the final step in the ML workflow as mentioned in the video?

• Data retrieval

• Training

• Deployment

• Model evaluation

Why is model evaluation important before deployment?

• It automatically updates the pipeline root folder.

• It reduces the time taken for deployment.

• It determines if the model should be deployed based on training results.

• It ensures the model is trained on the latest data.