Data Science Model Deployments and Cloud Computing on GCP - Lab - Custom Model Training Using SDK and Model Registries

The video tutorial demonstrates how to complete a manual training job using Vertex AI's web console and then replicate the process using Python's SDK. It covers setting up the Python SDK, Docker image, and service account, followed by executing a custom training job. Finally, it explains how to upload the trained model to Vertex AI's model registry, highlighting the importance of versioning and labeling models.

What is the purpose of the folder created in the GCS bucket?

Describe the process of submitting a manual training job using the Vertex AI web console.

What steps are involved in running a custom training job using Python's SDK?

What is the significance of the service account in the training job process?

Explain the parameters required for the custom container training job function.

What is the role of the model registry in Vertex AI?

How do you upload a model to the Vertex AI model registry?