Data Science Model Deployments and Cloud Computing on GCP - Lab - Pipeline Execution in Kubeflow

The video tutorial guides viewers through setting up a Jupyter Lab notebook for a Cube Flow Pipeline. It covers creating a new notebook, installing dependencies, defining components, and executing a pipeline. The tutorial also explains how to monitor the pipeline execution using Vertex AI, highlighting key steps such as data fetching, component definition, and pipeline triggering. The entire process is demonstrated with practical examples, ensuring viewers understand each step involved in setting up and running a Cube Flow Pipeline.

How can you check the logs of the pipeline execution?

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What is the estimated time for the entire pipeline to execute, and why does it take that long?

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