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Merge pull request #903 from iterative/engine/remove-indices
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cmd ref: remove unnecessary indices?
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jorgeorpinel committed Jan 8, 2020
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24 changes: 8 additions & 16 deletions public/static/docs/tutorials/index.md
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# Tutorials

- [Interactive Tutorials](/doc/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.

- [Data Versioning](/doc/tutorials/versioning) <br/> Using DVC commands to work
with multiple versions of datasets and ML models.

- [Stages and Pipelines](/doc/tutorials/pipelines) <br/> Using DVC commands to
build a simple ML pipeline.

- [Comprehensive Tutorial](/doc/tutorials/deep) <br/> Introduces DVC
step-by-step, while additionally explaining in great detail the motivation and
what's happening internally.

- [Community Tutorials](/doc/tutorials/community) <br/> Blog posts from the
community which show how they use DVC in their ML scenarios.
Beyond the _Get Started_ guide, our tutorials provide similarly reproducible
walkthroughs on more advanced topics. They also come in different formats such
as the [interactive](/doc/tutorials/interactive) ones (to try and learn DVC
without installing it), and from different sources like our official ones and
those produced by the DVC [community](/doc/tutorials/community).

Please choose from the navigation sidebar to the left, or click the `Next`
button below ↘
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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.
Try and learn DVC without having to install it locally!

## Basic Concepts

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# Use Cases

We provide short articles on common ML workflow or data management scenarios
that DVC can help with or improve. These include a justified context (usually
extracted from real-life cases), and the approaches to solving them combine
several features and commands of DVC, as well as other appropriate tools. They
range from basic to more advanced:

- [Data Versioning](/doc/use-cases/versioning-data-and-model-files) describes
our most primary use: tracking and versioning large files with Git + DVC.
- [Sharing Data and Model Files](/doc/use-cases/sharing-data-and-model-files)
goes over the basic collaboration possibilities enabled by DVC.
- [Shared Development Server](/doc/use-cases/shared-development-server) provides
instructions to setup a single development machine for teams that prefer so.
- [Data Registries](/doc/use-cases/data-registries) explains how to use a
<abbr>DVC repository<abbr> as a shared hub for reusing datasets or models
among several projects.

> This list of use cases is **not** exhaustive. We keep reviewing our docs and
> will include interesting scenarios that surface in the community. Please,
> [contact us](/support) if you need help or have suggestions!
Use cases are not written to be run end-to-end. For more general, hands-on
experience with DVC, we recommend following the [Get Started](/doc/get-started),
and/or [Tutorials](/doc/tutorials) first. These include prepared datasets,
source code files, and example commands that can be copied and pasted into
terminal. These use cases also link to our more general
[User Guide](/doc/user-guide), and technical
[command references](/doc/command-reference).
that DVC can help with or improve. These include the motivating context (usually
extracted from real-life cases); And the approaches to solving them can combine
several features of DVC. Use cases are not written to be run end-to-end. For
more general, hands-on experience with DVC, we recommend following the
[Get Started](/doc/get-started), and/or [Tutorials](/doc/tutorials) first.

> We keep reviewing our docs and will include interesting scenarios that surface
> in the community. Please, [contact us](/support) if you need help or have
> suggestions!
Our use cases range from basic to more advanced. Please choose from the
navigation sidebar to the left, or click the `Next` button below ↘
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guides related to contributing to this
[open-source project](https://github.com/iterative/dvc).

- [DVC Files and Directories](/doc/user-guide/dvc-files-and-directories)
describes the internal `.dvc/` directory and it's contents.
- [DVC-File Format](/doc/user-guide/dvc-file-format) explains how the special
metafiles that DVC creates to track <abbr>project</abbr> data are constructed.
- [Dvcignore](/doc/user-guide/dvcignore) explains how to exclude files from DVC
operations with the optional `.dvcignore` file.
- [Update a Tracked File](/doc/use-cases/updating-tracked-files) explains how to
modify protected data under DVC control (related to the `cache.type` option of
DVC).
- [Large Dataset Optimization](/doc/user-guide/large-dataset-optimization)
covers the topic of how <abbr>caches</abbr> data can be automatically file
linked to the workspace to avoid duplication of storage or slow and
unnecessary file operations.
- [External Dependencies](/doc/user-guide/external-dependencies) goes over the
ways in which DVC can keep track of files external to the project (or even
remote on a network or cloud location) as
[DVC-file](/doc/user-guide/dvc-files-and-directories) dependencies.
- [Managing External Data](/doc/user-guide/managing-external-data) explains both
external outputs and external cache.
- [Contributing](/doc/user-guide/contributing) is related with getting involved
directly in developing DVC. Contributors are very welcomed in our
[community](/support)!

The remaining guides are about optional productivity enhancements and other tips
and tools related to advanced usage of DVC.
Please choose from the navigation sidebar to the left, or click the `Next`
button below ↘

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