-
Notifications
You must be signed in to change notification settings - Fork 4.5k
restructure ml overview website page #25607
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Conversation
|
@damccorm Hi Danny, I made an additional edit to update the overview page of ML and to include model validation. Please have a look |
damccorm
left a comment
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Could you please add the license to the image file (see https://github.com/apache/beam/blob/master/website/www/site/static/images/ml-workflows.svg?short_path=6c34014 for example)
|
|
||
| ## Model validation | ||
|
|
||
| Model validation allows you to benchmark your model’s performance against an unseen dataset. You can extract chosen metrics, create visualizations, log metadata, and compare the performance of different models with the end goal of validating whether your model is ready to deploy. Beam provides support for running model evaluation on a TensorFlow model directly inside your pipeline. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
| Model validation allows you to benchmark your model’s performance against an unseen dataset. You can extract chosen metrics, create visualizations, log metadata, and compare the performance of different models with the end goal of validating whether your model is ready to deploy. Beam provides support for running model evaluation on a TensorFlow model directly inside your pipeline. | |
| Model validation allows you to benchmark your model’s performance against a previously unseen dataset. You can extract chosen metrics, create visualizations, log metadata, and compare the performance of different models with the end goal of validating whether your model is ready to deploy. Beam provides support for running model evaluation on a TensorFlow model directly inside your pipeline. |
Small wording nit
|
Thanks, this LGTM other than the 2 small comments |
|
Thanks @damccorm. I made the requested changes |
|
retest this please |
|
(comment is for bot) |
|
Run RAT PreCommit |
damccorm
left a comment
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks!
* restructure ml overview website page * small edits ML website
Restructure ML overview website page and add explanation about model validation
Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:
addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, commentfixes #<ISSUE NUMBER>instead.CHANGES.mdwith noteworthy changes.See the Contributor Guide for more tips on how to make review process smoother.
To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md
GitHub Actions Tests Status (on master branch)
See CI.md for more information about GitHub Actions CI.