Skip to content
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.

Sign up for GitHub

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

Jump to bottom

Added APC colab notebook link. #97

Merged
merged 1 commit into from
Feb 25, 2020
Merged

Added APC colab notebook link. #97

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
2 changes: 1 addition & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
ArangoML Pipeline is a common and extensible Metadata Layer for Machine Learning Pipelines which allows Data Scientists and [DataOps](https://en.wikipedia.org/wiki/DataOps) to manage all information related to their ML pipeline in one place.

**News:**
[ArangoML Pipeline Cloud](https://www.arangodb.com/2020/01/arangoml-pipeline-cloud-manage-machine-learning-metadata/) is offering a no-setup, free-to-try managed service for ArangpML Pipeline.
[ArangoML Pipeline Cloud](https://www.arangodb.com/2020/01/arangoml-pipeline-cloud-manage-machine-learning-metadata/) is offering a no-setup, free-to-try managed service for ArangpML Pipeline. A [ArangoML Pipeline Cloud tutorial](https://colab.research.google.com/github/arangoml/arangopipe/blob/master/examples/Arangopipe_with_TensorFlow_Beginner_Guide.ipynb#) is also available without any installation or Signup:

## Introduction
When productizing Machine Learning Pipelines (e.g., [TensorFlow Extended](https://www.tensorflow.org/tfx/guide) or [Kubeflow](https://www.kubeflow.org/))
Expand Down