Data Science Model Deployments and Cloud Computing on GCP - Lab - Deploy Training Code to App Engine

This video tutorial covers deploying an application to Google App Engine, training a machine learning model, and deploying the serialized model to Google Cloud Storage. It explains the setup of necessary files, the main Python script, and the training process, including hyperparameter tuning. The video also demonstrates how to check logs and handle errors, concluding with a preview of serving predictions in the next video.

What steps should be taken before executing the Python script for model training?

How can you check the logs of the deployed application?

What happens if an invalid parameter is passed to the solver in logistic regression?

What will be covered in the next video after deploying the model?