readme: update description, summary, links

README.rst

@@ -28,17 +28,18 @@
 |
 
 **Data Science Version Control** or **DVC** is an **open-source** tool for data science and
-machine learning projects. With a simple and flexible Git-like architecture and interface it
-helps data scientists:
+machine learning projects. Key features:
 
-#. manage **machine learning models** - versioning, including data sets and transformations (scripts) that were
-   used to generate models;
+#. simple **command line** Git-like exprerience. Does not require installing and maintaining
+   any databases. Does not depend on any proprietary online services;
 
-#. make projects **reproducible**;
+#. it manages and versions **datasets** and **machine learning models**. Data is saved in
+   S3, Google cloud, Azure, SSH server, HDFS or even local HDD RAID;
 
-#. make projects **shareable**;
+#. it makes projects **reproducible** and **shareable**, it helps answering question how
+   the model was build;
 
-#. manage experiments with branching and **metrics** tracking;
+#. it helps manage experiments with Git tags or branches and **metrics** tracking;
 
 It aims to replace tools like Excel and Docs that are being commonly used as a knowledge repo and
 a ledger for the team, ad-hoc scripts to track and move deploy different model versions, ad-hoc
@@ -50,14 +51,30 @@ data file suffixes and prefixes.
 How DVC works
 =============
 
-DVC is compatible with Git for storing code and the dependency graph (DAG), but not data files cache.
-To store and share data files cache DVC supports remotes - any cloud (S3, Azure, Google Cloud, etc) or any on-premise
-network storage (via SSH, for example).
+We encourage you to read our `Get Started <https://dvc.org/doc/get-started>`_ to better understand what DVC
+is and how does it fit your scenarios.
+
+The easiest (but not perfect!) way to describe it: DVC is Git (or Git-lfs to be precise) + ``makefiles``
+tailored specifically for ML and Data Science scenarios.
+
+#. ``Git/Git-lfs`` part - DVC helps you storing and sharing data artifacts, models. It connects them with your
+   Git repository.
+#. ``Makefiles`` part - DVC describes how one data or model artifact was build from another data. 
+
+DVC usually runs along with Git. Git is used as usual to store and version code and DVC meta-files. DVC helps
+to store data and model files seamlessly out of Git while preserving almost the same user experience as if they
+were stored in Git itself. To store and share data files cache DVC supports remotes - any cloud (S3, Azure,
+Google Cloud, etc) or any on-premise network storage (via SSH, for example). It does not require 
 
 .. image:: https://dvc.org/static/img/flow.gif
    :target: https://dvc.org/static/img/flow.gif
    :alt: how_dvc_works
 
+DVC pipelines (aka computational graph) feature connects code and data together. In a very explicit way you can
+specify, run, and save information that a certain command with certain dependencies needs to be run to produce
+a model. See the quick start section below or check `Get Started <https://dvc.org/doc/get-started>`_ tutorial to
+learn more.
+
 Quick start
 ===========
 
@@ -66,7 +83,7 @@ Please read `Get Started <https://dvc.org/doc/get-started>`_ for the full versio
 +-----------------------------------+-------------------------------------------------------------------+
 | Step                              | Command                                                           |
 +===================================+===================================================================+
-| Track code and data together      | | ``$ git add train.py``                                          |
+| Track data                        | | ``$ git add train.py``                                          |
 |                                   | | ``$ dvc add images.zip``                                        |
 +-----------------------------------+-------------------------------------------------------------------+
 | Connect code and data by commands | | ``$ dvc run -d images.zip -o images/ unzip -q images.zip``      |
@@ -79,27 +96,30 @@ Please read `Get Started <https://dvc.org/doc/get-started>`_ for the full versio
 |                                   | | ``$ git commit -m 'The baseline model'``                        |
 |                                   | | ``$ git push``                                                  |
 +-----------------------------------+-------------------------------------------------------------------+
-| Share data and ML models          | | ``$ dvc remote add myremote s3://mybucket/image_cnn``           |
-|                                   | | ``$ dvc config core.remote myremote``                           |
+| Share data and ML models          | | ``$ dvc remote add myremote -d s3://mybucket/image_cnn``        |
 |                                   | | ``$ dvc push``                                                  |
 +-----------------------------------+-------------------------------------------------------------------+
 
 Installation
 ============
 
-There are three options to install DVC: ``pip``, Homebrew, or an OS-specific package:
+Read this `instruction <https://dvc.org/doc/get-started/install>`_ to get more details. There are three
+options to install DVC: ``pip``, Homebrew, or an OS-specific package:
 
 pip (PyPI)
 ----------
 
-Stable
-^^^^^^
 .. code-block:: bash
 
    pip install dvc
 
-Development
-^^^^^^^^^^^
+Depending on the remote storage type you plan to use to keep and share your data you might need to specify
+one of the optional dependencies: ``s3``, ``gs``, ``azure``, ``ssh``. Or ``all_remotes`` to include them all.
+The command should look like this: ``pip install dvc[s3]`` - it installs the ``boto3`` library along with
+DVC to support the AWS S3 storage.
+
+To install the development version, run:
+
 .. code-block:: bash
 
    pip install git+git://github.com/iterative/dvc