From 5926097977b62d2056b76fe2ed6731abf8220f57 Mon Sep 17 00:00:00 2001 From: Noah Date: Fri, 31 Jul 2020 14:10:27 -0400 Subject: [PATCH 1/2] Update data-pipelines.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Saw a typo and got a bit carried away. I'm a big DVC fan! 💫 👨‍💻 --- content/docs/start/data-pipelines.md | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/content/docs/start/data-pipelines.md b/content/docs/start/data-pipelines.md index 45ac59a48e..c0c59b4ccf 100644 --- a/content/docs/start/data-pipelines.md +++ b/content/docs/start/data-pipelines.md @@ -292,17 +292,14 @@ prepare: DVC pipelines (`dvc.yaml` file, `dvc run`, and `dvc repro` commands) solve a few important problems: -- _Automation_ - run sequence of steps in a "smart" way that makes iterating on - the project faster. It automatically determines which parts of a project need - to be run, it caches "runs" and results — all to avoid running the same stage - again. -- _Reproducibility_ - it can describe and capture what data should be used and - what commands to run to produce an ML model, for example. It's described and - captured in way that is easy to put into Git. It means that it's easy to - version and share. +- _Automation_ - run a sequence of steps in a "smart" way to iterate on your + project faster. DVC caches "runs" and results in stages to avoid unnecessary + re-runs. +- _Reproducibility_ - YAML files describe and capture what data to use and + what commands to run to produce an ML model. Storing these files in Git + makes it easy to version and share. - _Continuous Delivery and Continuous Integration (CI/CD) for ML_ - describing - project in way that it can be reproduced (built) is the fist necessary step - before introducing CI/CD systems. + reproducible ML pipelines (builds) facilitates CI/CD systems. ## Visualize From 2dfffeff28a18e639618556bc6badf7cc5fc4149 Mon Sep 17 00:00:00 2001 From: "Restyled.io" Date: Fri, 31 Jul 2020 18:23:25 +0000 Subject: [PATCH 2/2] Restyled by prettier --- content/docs/start/data-pipelines.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/docs/start/data-pipelines.md b/content/docs/start/data-pipelines.md index c0c59b4ccf..a94ce6390a 100644 --- a/content/docs/start/data-pipelines.md +++ b/content/docs/start/data-pipelines.md @@ -292,12 +292,12 @@ prepare: DVC pipelines (`dvc.yaml` file, `dvc run`, and `dvc repro` commands) solve a few important problems: -- _Automation_ - run a sequence of steps in a "smart" way to iterate on your +- _Automation_ - run a sequence of steps in a "smart" way to iterate on your project faster. DVC caches "runs" and results in stages to avoid unnecessary re-runs. -- _Reproducibility_ - YAML files describe and capture what data to use and - what commands to run to produce an ML model. Storing these files in Git - makes it easy to version and share. +- _Reproducibility_ - YAML files describe and capture what data to use and what + commands to run to produce an ML model. Storing these files in Git makes it + easy to version and share. - _Continuous Delivery and Continuous Integration (CI/CD) for ML_ - describing reproducible ML pipelines (builds) facilitates CI/CD systems.