Simplify the index page and reorganize the interactive tutorials

server.js

@@ -56,9 +56,9 @@ app.prepare().then(() => {
       })
       res.end()
     } else if (pathname == '/doc/tutorial') {
-      // TMP: path /doc/tutorial -> /doc/tutorials/tutorial
+      // path /doc/tutorial -> /doc/tutorials
       res.writeHead(302, {
-        Location: req.url.replace('/doc/tutorial', '/doc/tutorials/tutorial')
+        Location: req.url.replace('/doc/tutorial', '/doc/tutorials')
       })
       res.end()
     } else if (/^\/doc.*/i.test(pathname)) {

src/Documentation/sidebar.json

@@ -31,13 +31,9 @@
     "slug": "tutorials",
     "source": "tutorials/index.md",
     "children": [
-      {
-        "slug": "basics",
-        "label": "Interactive: Basics"
-      },
       {
         "slug": "interactive",
-        "label": "Interactive: Tutorials"
+        "label": "Interactive Tutorials"
       },
       {
         "slug": "versioning",
@@ -60,6 +56,10 @@
           "reproducibility",
           "sharing-data"
         ]
+      },
+      {
+        "slug": "community",
+        "label": "Community Tutorials"
       }
     ]
   },

static/docs/tutorials/community.md

@@ -0,0 +1,23 @@
+# Community Tutorials
+
+Blog posts from the community which show how they use DVC in their ML scenarios:
+
+- [Data Version Control Tutorial](https://blog.dataversioncontrol.com/data-version-control-tutorial-9146715eda46)
+
+- [Creating an awesome project using DVC and DAGsHub](https://dagshub.com/docs/overview/)
+
+- [Using DVC to create an efficient version control system for data projects](https://medium.com/qonto-engineering/using-dvc-to-create-an-efficient-version-control-system-for-data-projects-96efd94355fe)
+
+- [Introduction to using DVC to manage machine learning project datasets](https://techsparx.com/software-development/ai/dvc/simple-example.html)
+
+- [Managing versioned machine learning datasets in DVC, and easily share ML projects with colleagues](https://techsparx.com/software-development/ai/dvc/versioning-example.html)
+
+- [A walkthrough of DVC](https://blog.codecentric.de/en/2019/03/walkthrough-dvc/)
+
+- [DVC dependency management](https://blog.codecentric.de/en/2019/08/dvc-dependency-management/)
+
+- [How to use data version control (dvc) in a machine learning project](https://towardsdatascience.com/how-to-use-data-version-control-dvc-in-a-machine-learning-project-a78245c0185)
+
+- [My first try at DVC](https://stdiff.net/MB2019051301.html)
+
+- [Effective Management of your Machine Learning Laboratory](https://www.linkedin.com/pulse/effective-management-your-machine-learning-laboratory-ulaganathan/)

static/docs/tutorials/index.md

@@ -1,74 +1,18 @@
 # Tutorials
 
-## Basic Concepts
+- [Interactive Tutorials](/docs/tutorials/interactive) <br/> Interactive lessons
+  and tutorials on [Katacoda](https://katacoda.com/dvc) that explain the basic
+  concepts of DVC and show how to use it in simple ML scenarios.
 
-Learn basic concepts and features of DVC with interactive lessons:
+- [Data Versioning](/docs/tutorials/versioning) <br/> Using DVC commands to work
+  with multiple versions of datasets and ML models.
 
-1. [Data Management](https://katacoda.com/dvc/courses/basics/data) <br/> The
-   core function of DVC is data tracking and management. Let's see how to do it.
+- [Stages and Pipelines](/docs/tutorials/pipelines) <br/> Using DVC commands to
+  build a simple ML pipeline.
 
-2. [Getting the Best Performance](https://katacoda.com/dvc/courses/basics/performance)
-   <br/> It is important to optimize the DVC setup for having the best
-   performance with handling big data files.
+- [Longer Tutorial](/docs/tutorials/tutorial) <br/> Introduces DVC step-by-step,
+  while additionally explaining in great detail the motivation and what's
+  happening internally.
 
-3. [Tracking Data Versions](https://katacoda.com/dvc/courses/basics/versioning)
-   <br/> DVC takes advantage of GIT's versioning features to keep track of the
-   data versions.
-
-4. [Sharing Data](https://katacoda.com/dvc/courses/basics/sharing) <br/> DVC
-   facilitates sharing of data between different people that work on the same
-   project.
-
-5. [Stages And Pipelines](https://katacoda.com/dvc/courses/basics/pipelines)
-   <br/> DVC has a built-in way to connect ML steps into a DAG and run the full
-   pipeline end-to-end.
-
-## Interactive Tutorials
-
-Learn how DVC can be used in simple ML scenarios:
-
-1. [Data Versioning](https://katacoda.com/dvc/courses/tutorials/versioning)
-   <br/> Using DVC commands to work with multiple versions of datasets and ML
-   models.
-
-2. [Stages and Pipelines](https://katacoda.com/dvc/courses/tutorials/pipelines)
-   <br/> Using DVC commands to build a simple ML pipeline.
-
-3. [dvc fetch](https://katacoda.com/dvc/courses/examples) <br/> We will use an
-   example project with some data, code, ML models, pipeline stages, as well as
-   a few Git tags. Then we will see what happens with dvc fetch as we switch
-   from tag to tag.
-
-## Native Tutorials
-
-1. [Data Versioning](/docs/tutorials/versioning) <br/> Using DVC commands to
-   work with multiple versions of datasets and ML models.
-
-2. [Stages and Pipelines](/docs/tutorials/pipelines) <br/> Using DVC commands to
-   build a simple ML pipeline.
-
-3. [Longer Tutorial](/docs/tutorials/tutorial) <br/> Introduces DVC
-   step-by-step, while additionally explaining in great detail the motivation
-   and what's happening internally.
-
-## Community Tutorials
-
-- [Data Version Control Tutorial](https://blog.dataversioncontrol.com/data-version-control-tutorial-9146715eda46)
-
-- [Creating an awesome project using DVC and DAGsHub](https://dagshub.com/docs/overview/)
-
-- [Using DVC to create an efficient version control system for data projects](https://medium.com/qonto-engineering/using-dvc-to-create-an-efficient-version-control-system-for-data-projects-96efd94355fe)
-
-- [Introduction to using DVC to manage machine learning project datasets](https://techsparx.com/software-development/ai/dvc/simple-example.html)
-
-- [Managing versioned machine learning datasets in DVC, and easily share ML projects with colleagues](https://techsparx.com/software-development/ai/dvc/versioning-example.html)
-
-- [A walkthrough of DVC](https://blog.codecentric.de/en/2019/03/walkthrough-dvc/)
-
-- [DVC dependency management](https://blog.codecentric.de/en/2019/08/dvc-dependency-management/)
-
-- [How to use data version control (dvc) in a machine learning project](https://towardsdatascience.com/how-to-use-data-version-control-dvc-in-a-machine-learning-project-a78245c0185)
-
-- [My first try at DVC](https://stdiff.net/MB2019051301.html)
-
-- [Effective Management of your Machine Learning Laboratory](https://www.linkedin.com/pulse/effective-management-your-machine-learning-laboratory-ulaganathan/)
+- [Community Tutorials](/docs/tutorials/community) <br/> Blog posts from the
+  community which show how they use DVC in their ML scenarios.

static/docs/tutorials/interactive.md

@@ -1,15 +1,46 @@
 # Interactive Tutorials
 
+Interactive lessons and tutorials on [Katacoda](https://katacoda.com/dvc) that
+explain the basic concepts of DVC and show how to use it in simple ML scenarios.
+
+## Basic Concepts
+
+Learn basic concepts and features of DVC with interactive lessons:
+
+1. [Data Management](https://katacoda.com/dvc/courses/basics/data) <br/> The
+   core function of DVC is data tracking and management. Let's see how to do it.
+
+2. [Getting the Best Performance](https://katacoda.com/dvc/courses/basics/performance)
+   <br/> It is important to optimize the DVC setup for having the best
+   performance with handling big data files.
+
+3. [Tracking Data Versions](https://katacoda.com/dvc/courses/basics/versioning)
+   <br/> DVC takes advantage of GIT's versioning features to keep track of the
+   data versions.
+
+4. [Sharing Data](https://katacoda.com/dvc/courses/basics/sharing) <br/> DVC
+   facilitates sharing of data between different people that work on the same
+   project.
+
+5. [Stages And Pipelines](https://katacoda.com/dvc/courses/basics/pipelines)
+   <br/> DVC has a built-in way to connect ML steps into a DAG and run the full
+   pipeline end-to-end.
+
+## Simple ML Scenarios
+
 Learn how DVC can be used in simple ML scenarios:
 
-1. [Data Versioning](https://katacoda.com/dvc/courses/tutorials/versioning)
-   <br/> Using DVC commands to work with multiple versions of datasets and ML
-   models.
+- [Data Versioning](https://katacoda.com/dvc/courses/tutorials/versioning) <br/>
+  Using DVC commands to work with multiple versions of datasets and ML models.
+
+- [Stages and Pipelines](https://katacoda.com/dvc/courses/tutorials/pipelines)
+  <br/> Using DVC commands to build a simple ML pipeline.
+
+## Examples
 
-2. [Stages and Pipelines](https://katacoda.com/dvc/courses/tutorials/pipelines)
-   <br/> Using DVC commands to build a simple ML pipeline.
+Interactive examples about using DVC commands and other features of DVC.
 
-3. [dvc fetch](https://katacoda.com/dvc/courses/examples) <br/> We will use an
-   example project with some data, code, ML models, pipeline stages, as well as
-   a few Git tags. Then we will see what happens with dvc fetch as we switch
-   from tag to tag.
+- [dvc fetch](https://katacoda.com/dvc/courses/examples/fetch) <br/> We will use
+  an example project with some data, code, ML models, pipeline stages, as well
+  as a few Git tags. Then we will see what happens with dvc fetch as we switch
+  from tag to tag.