This example shows how you can use the same feature definitions
in Hamilton in an offline setting and use them in an online setting.

Assumptions:
 - the API request can provide the same raw data that training provides.
 - if you have aggregation features, you need to store the training result
for them, and provide them to the online side.

This example shows how one might use Hamilton to compute
features in an offline and online fashion. The assumption here
is that the request passed into the API has all the raw data
required to compute features.

This example also shows how one might "override" some values
that are required for computing features, in this example they
are `age_mean` and `age_std_dev`. This can be required when you
computing aggregation features does not make sense at
inference time.

I think that this makes things clearer what this file is, and is a lightweight
way to register feature sets that are used by a model.

To help set the tone and explain what feature engineering is,
as well as more context about the scenarios and the task.

I expand on the docs, and hopefully explain it a bit more that
is understandable to a novice.

As a way to show functionality that can be used to highlight
that they should be overridden in an online setting.