From 94584a82e272385801fbb26708e8617562fd2cbc Mon Sep 17 00:00:00 2001 From: Jorge Orpinel Date: Tue, 12 Nov 2019 00:25:32 -0500 Subject: [PATCH] use-cases: add marketing line to data registry case per https://github.com/iterative/dvc.org/pull/679#issuecomment-552621599 --- static/docs/use-cases/data-registry.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/static/docs/use-cases/data-registry.md b/static/docs/use-cases/data-registry.md index 933e304e2b..a5eead5b21 100644 --- a/static/docs/use-cases/data-registry.md +++ b/static/docs/use-cases/data-registry.md @@ -26,17 +26,15 @@ data processes. The advantages of using a DVC **data registry** project are: -- Data as code: Improve _lifecycle management_ with - [versioning](/doc/use-cases/data-and-model-files-versioning) of simple - directory structures (without ad-hoc conventions); Any version of the data or - results tracked by a DVC registry can be used in other projects at any time. - Leverage Git and Git hosting (e.g. GitHub) features such as change history, +- Data as code: Improve _lifecycle management_ with versioning of simple + directory structures (like Git for your cloud storage), without ad-hoc + conventions. Leverage Git and Git hosting features such as change history, branching, pull requests, reviews, and even continuous deployment of ML models. - Reusability: Reproduce and organize _feature stores_ with a simple CLI (`dvc get` and `dvc import` commands, similar to software package management systems like `pip`). -- Persistence: The DVC registry controlled +- Persistence: The DVC registry-controlled [remote storage](/doc/command-reference/remote) (e.g. an S3 bucket) improves data security. There are less chances someone can delete or rewrite a model, for example.