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Solution: Run and evaluate a model pipeline job

Solution: Run and evaluate a model pipeline job - Google Cloud Platform Tutorial

From the course: Google Cloud Platform for Machine Learning Essential Training

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Solution: Run and evaluate a model pipeline job

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Did you choose to run the Pipeline template in a notebook, or did you run it in the web UI? Admittedly, running in a notebook is more advanced because you do not get a visualization of the DAG while it's running. You can see after you do all the setup, including associating the storage, which is Google Cloud storage buckets, then you are going to -- and I'll just scroll down here. So you need Vertex AI Pipelines, GCP components, Vertex AI training model, resources, and Endpoints. And this is using a public data set from BigQuery. So first you install the libraries. Now, question, why is kfp2 being installed? Do you remember from the description? This is Kubeflow. So this is a library which sits on top of Google Kubernetes engine so that you can have a cluster of containers for efficiency. So then you have to just do more setup, set up your project ID, your region. This is just a convenience so you don't have a collision problem with the shared account, a UUID then authenticating…

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