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You'll need Git to run the commands in this guide. Also, if DVC is not installed, please follow these instructions to do so.

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 by running:

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

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

The idea for this project is a simplified version of our Deep Dive Tutorial. 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.

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 data artifacts the same way you capture code
  • 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're in the right place! Click the Next button below to start โ†˜

๐Ÿ› Found an issue? Let us know! Or fix it:

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