# For Data Scientists

With TinyChain, data scientists can easily analyze live replicas of a production database, eliminating the need to copy entire tables and databases out of the platform which tracks and enforces ownership of the data. This also eliminates the need to maintain a separate platform (e.g. TensorFlow Serving) in order to serve models. TinyChain also allows the construction of stateful models, like a recurrent neural network (RNN) with a per-user state in memory, or a model which updates a database when it encounters an unexpected input.


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tinychain.net/use-cases/for-data-scientists.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.