docs: Reargange

Signed-off-by: John Andersen <john.s.andersen@intel.com>

Author: John Andersen

README.md

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+# Data Flow Facilitator for Machine Learning (dffml)
+
+[![Build Status](https://travis-ci.org/intel/dffml.svg?branch=master)](https://travis-ci.org/intel/dffml) [![CII](https://bestpractices.coreinfrastructure.org/projects/2594/badge)](https://bestpractices.coreinfrastructure.org/projects/2594)
+
+DFFML provides APIs for dataset generation and storage, and model definition
+using any machine learning framework, from high level down to low level use is
+supported.
+
+The goal of DFFML is to build a community driven library of plugins for dataset
+generation and model definition. So that we as developers and researchers can
+quickly and easily plug and play various pieces of data with various model
+implementations.
+
+DFFML allows users to take advantage of Python's `asyncio` library in order to
+build applications which interact in deterministic ways with external data
+sources and syncs (think pub/sub, websockets, grpc streams). Writing with
+`asyncio` in the loop (huh-huh) makes testing MUCH MUCH EASIER. `asyncio` usage
+also means that when generating datasets, we can do everything concurrently (or
+in parallel if you want to us an executor) making generating a dataset from
+scratch very fast, and best of all, clean error handling if things go wrong.
+
+Here's a quick demo showing how DFFML can be used to train on the iris dataset.
+The more we build up the library of plugins (which anyone can maintain, they
+don't have to be contributed upstream unless you want to) the more variations on
+model implementations and feature data generators we all have to work with.
+
+![Demo](https://github.com/intel/dffml/raw/master/docs/images/iris_demo.gif)
+
+Right now we've released a wrapper around the Tensorflow DNN estimator, and a
+set of feature generators which gather data from git repositories.
+
+## Installation
+
+DFFML currently should work with Python 3.6. However, only Python 3.7 is
+officially supported. This is because there are a lot of nice helper methods
+Python 3.7 implemented that we intend to use instead of re-implementing.
+
+```python
+python3.7 -m pip install -U dffml
+```
+
+You can also install the Features for Git Version Control, and Models for
+Tensorflow Library all at once.
+
+- [DFFML Features for Git Version Control](feature/git/README.md)
+- [DFFML Models for Tensorflow Library](model/tensorflow/README.md)
+
+If you want a quick how to on the iris dataset head to the
+[DFFML Models for Tensorflow Library](model/tensorflow/README.md) repo.
+
+```python
+python3.7 -m pip install -U dffml[git,tensorflow]
+```
+
+## Usage
+
+See [DFFML Models for Tensorflow Library](model/tensorflow/README.md) repo
+until documentation here is updated with a generic example.
+
+## Documentation
+
+Start with [Architechture](docs/ARCHITECHTURE.md).
+
+## License
+
+dffml is distributed under the [MIT License](LICENSE).
+
+## Legal
+
+> This software is subject to the U.S. Export Administration Regulations and
+> other U.S. law, and may not be exported or re-exported to certain countries
+> (Cuba, Iran, Crimea Region of Ukraine, North Korea, Sudan, and Syria) or to
+> persons or entities prohibited from receiving U.S. exports (including
+> Denied Parties, Specially Designated Nationals, and entities on the Bureau
+> of Export Administration Entity List or involved with missile technology or
+> nuclear, chemical or biological weapons).

README.rst

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+# Is DFFML Right For Me?
+
+If you answer yes to any of these questions DFFML can make your life easier.
+
+- Dataset Generation
+
+  - Need to generate a dataset
+  - Need to run asynchronous operations in order to gather dataset (http
+    requests, interaction with command line utilities, etc.)
+
+- Models
+
+  - Want to quickly prototype how machine learning could be used on a dataset
+    without writing a model
+  - Need to write a finely tuned model by interacting with low level APIs of
+    popular machine learning frameworks.
+
+- Storage
+
+  - Need a way to use datasets which could be stored in different locations or
+    formats.
+
+# About
+
+DFFML facilitates data generation, model creation, and use of models via
+services. See [Architecture](ARCHITECTURE.md) to learn how it works.
+
+- Facilitates data collection, model creation, and use of models via services.
+- Provides plumbing to facilitate the collection of feature data to create
+  datasets.
+- Allows developers to define their ML models via a standardized API.
+
+  - This let's users try different libraries / models to compare performance.
+
+- Plugin based
+
+  - Features which gather feature data (Number of Git Authors, etc.)
+  - Models which expose ML models via the standard API (Tensorflow, Scikit,
+    etc.)
+  - Sources which load and store feature data (CSV, JSON, MySQL, etc.)
+
+The plumbing DFFML provides enables users to swap out models and features,
+in order to quickly prototype.