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# Agenda

In the next few sections we will build a simple natural language processing
(NLP) project from scratch. If you'd like to get the final result or have some
issues along the way, you can download the fully reproducible
[GitHub project](https://github.com/iterative/example-get-started) with:
In the next few sections we'll build a simple natural language processing (NLP)
project from scratch. If you'd like to get the final result or have any issues
along the way, you can download the fully reproducible
[GitHub project](https://github.com/iterative/example-get-started) by running:

```dvc
$ git clone https://github.com/iterative/example-get-started
```

Otherwise, bear with us and we will introduce the basic DVC concepts and get to
the same result together!
Otherwise, bear with us and we'll introduce some basic DVC concepts to get the
same results together!

The idea of the project is a simplified version of the
[Tutorial](/doc/tutorials/deep). It explores the NLP problem of predicting tags
for a given StackOverflow question. For example, we want a classifier that can
predict posts about the Python language by tagging them `python`.
The idea of the project is a simplified version of our
[Deep Dive Tutorial](/doc/tutorials/deep). It explores the NLP problem of
predicting tags for a given StackOverflow question. For example, we might want a
classifier that can classify (or predict) posts about Python by tagging them
with `python`.

![](/static/img/example-flow-2x.png)

Do not let the NLP nature of the example discourage you from using DVC in other
Data Science areas. There was no strong reason behind picking the NLP area. On
contrary, DVC is designed to be agnostic of frameworks, languages, etc. If you
have data files or datasets and/or you produce other data files, models,
datasets and you want to:
This is a natural language processing context, but NLP isn't the only area of
data science where DVC can help. DVC is designed to be agnostic of frameworks,
languages, etc. If you have data files or datasets and/or you produce data
files, models, or datasets and you want to:

- Capture and save those <abbr>data artifacts</abbr> the same way we capture
- Capture and save those <abbr>data artifacts</abbr> the same way you capture
code
- Track and switch between different versions of the data easily
- Be able to answer the question of how data artifacts (e.g. ML models) were
built in the first place
- Be able to compare them
- Bring best practices to your team and get everyone on the same page
- Track and switch between different versions of data easily
- Understand how data artifacts (e.g. ML models) were built in the first place
- Be able to compare models to each other
- Bring software best practices to your team and get everyone on the same page

Then you are in a good place! Click the `Next` button below to start ↘
Then you're in the right place! Click the `Next` button below to start ↘

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