Category: Additional permissions
Pipeline comparison – Pipelines using Kubeflow for Custom Models
Users may run multiple pipelines with different models or with a different sample of data. GCP provides users an option to compare the performance of different pipelines. In this exercise, we had built a pipeline to train random forest classifier model. Change random forest to any other model of your choice and run the pipeline
Pipeline – Pipelines using Kubeflow for Custom Models
Follow these steps to analyze the status of the pipeline job, artifacts, lineage and output: Step 1: Pipeline of custom model Open the link as shown in Figure 7.10 to navigate to the pipelines of Vertex AI. The pipeline will start executing and will take about 5 to 10 mins. All the four tasks in
Pipeline code walk through – Pipelines using Kubeflow for Custom Models
We will be using Python 3 notebook file to type commands, create a pipeline, compile and to run it. Follow the following mentioned steps to create a Python file and type the Python codes given in this section. Step 1: Create Python notebook file Once the workbench is created, open the Jupyterlab and follow the
Additional permissions – Pipelines using Kubeflow for Custom Models
We also need to grant additional permission to the service account associated with the compute engine of GCP since we are fetching data from BigQuery. Follow these steps to grant the required permission. Step 1: Open IAM and admin section Follow the steps mentioned in Figure 7.2 to add roles to the service account associated
What is machine learning pipeline – Introduction to Pipelines and Kubeflow
Introduction In the previous chapters, we worked on workbench of Vertex AI to train custom models including hyperparameter tuning using Vizer. In this chapter, we will get started with the pipelines of Vertex AI. We will understand what pipeline is, what is Kubeflow, what are the components of the pipeline, and how to configure and