feat(examples): Vertex Machine Learning Pipeline #66
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update with tests
update docs
caetano-colin
approved these changes
Jul 2, 2024
add discalimers
This reverts commit ea02006.
apeabody
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Sep 10, 2024
scripts/validate-requirements.sh
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| validate_bootstrap_step_external_repo | ||
| validate_bootstrap_step_external_repo | ||
| elif [[ "$TERRAFORM_LOCAL" == "true" ]]; then | ||
| validate_bootstrap_terraform_local |
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| validate_bootstrap_terraform_local | |
| validate_bootstrap_terraform_local |
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| file. | ||
| This example demonstrates the process of interactive coding and experimentation using the Google Vertex AI Workbench for data scientists. The guide outlines the creation of a machine learning (ML) pipeline within a notebook on a Google Vertex AI Workbench Instance. | ||
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| This environment is set up for interactive coding and experimentations. After the project is up, the vertex workbench is deployed from service catalog and the datascientis can use it to write their code including any experiments, data processing code and pipeline components. In addition, a cloud storage bucket is deployed to use as the storage for our operations. Optionally a composer environment is which will later be used to schedule the pipeline run on intervals. |
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| This environment is set up for interactive coding and experimentations. After the project is up, the vertex workbench is deployed from service catalog and the datascientis can use it to write their code including any experiments, data processing code and pipeline components. In addition, a cloud storage bucket is deployed to use as the storage for our operations. Optionally a composer environment is which will later be used to schedule the pipeline run on intervals. | |
| This environment is set up for interactive coding and experimentations. After the project is up, the vertex workbench will be deployed from the base environment module on (`/modules/base_env/main.tf` and the data scientists can use it to write their data processing code and pipeline components. In addition, a cloud storage bucket should be deployed to use as the storage for our operations. Optionally, a composer environment can be setup to schedule the pipeline run on intervals. |
| - The model is trained and deployed using the census income dataset. | ||
| - Deployment and monitoring occur in the production environment. | ||
| - A/B Testing: | ||
| - After successful pipeline runs, a new model version is deployed for A/B testing. | ||
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| ## Purpose |
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remove purpose section or correct the text
| @@ -420,519 +672,1534 @@ Run `terraform output cloudbuild_project_id` in the `0-bootstrap` folder to get | |||
| cd .. | |||
| ``` | |||
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| ## Running Terraform locally | |||
| ### VPC-SC - Infrastructure Deployment with Local Terraform - Only proceed with these if you have not used Cloud Build | |||
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Move the section VPC-SC - Infrastructure Deployment with Local Terraform - Only proceed with these if you have not used Cloud Build as a subsection to Prerequisites after the VPC-SC instructions for cloudbuild
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and remove the duplicated "Usage" sections that is right above it
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I created this issue as an improvement. I believe we having two separated READMEs will keep the doc more organized.
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This PR creates a Machine Learning Pipeline example with the following: