Update readme

New comparison

Author: sokolegg

Readme.rst

@@ -1,11 +1,9 @@
 |Banner|
 
-`Website <https://tatradev.com>`_
-• `Docs <https://tatradev.com>`_
-• `Blog <https://tatradev.com>`_
-• `Twitter <https://tatradev.com>`_
-• `Chat (Community & Support) <https://tatradev.com>`_
-• `Tutorial <https://tatradev.com>`_
+`Website <http://pipertool.org/>`_
+• `Docs <http://pipertool.org>`_
+• `Chat (Community & Support) <https://t.me/pipertool>`_
+• `Tutorials <http://pipertool.org>`_
 
 # TODO: add useful installation links, code coverage and test from CICD
 
@@ -39,40 +37,26 @@ Quick start
 Installation
 ============
 
-
-Snap (Snapcraft/Linux)
-----------------------
-
-
-Choco (Chocolatey/Windows)
---------------------------
-
-Brew (Homebrew/Mac OS)
-----------------------
-
-Conda (Anaconda)
-----------------
-
 pip (PyPI)
 ----------
 
+.. code-block:: bash
+  :caption: pip installation
+    pip install pipertool
+
 Comparison to related technologies
 ==================================
 
-#. Data Engineering tools such as `AirFlow <https://airflow.apache.org/>`_,
-   `Luigi <https://github.com/spotify/luigi>`_, and others - We use Airflow and Luigi as possible context for executors
-
-#. Opyrator -
+#. Jupyter - is the de facto experimental environment for most data scientists. However, it is desirable to write experimental code.
 
-#. Ansible -
-
-#. Kubernetes -
+#. Data Engineering tools such as `AirFlow <https://airflow.apache.org/>`_,
+   `Luigi <https://github.com/spotify/luigi>`_ -These are very popular ML pipeline build tools. Airflow can be connected to a kubernetes cluster or collect tasks through a simple PythonOperator. The downside is that their functionality is generally limited on this, that is, they do not provide ML modules out of the box. Moreover, all developments will still have to be wrapped in a scheduler and this is not always a trivial task. However, we like them and we use Airflow and Luigi as possible context for executors.
 
-#. Dagster -
+#. Azure ML / Amazon SageMaker / Google Cloud - Cloud platforms really allow you to assemble an entire system from ready-made modules and put it into operation relatively quickly. Of the minuses: high cost, binding to a specific cloud, as well as small customization for specific business needs. For a large business, this is the most logical option - to build an ML infrastructure in the cloud. We also maintain cloud options as posible ways for the deployment step.
 
-#. DVC, MLFlow, and others -
+#. DataRobot/Baseten - They offer an interesting, but small set of ready-made modules. However, in Baseten, all integration is implied in the kubernetes cluster. This is not always convenient and necessary for Proof-of-Concept. Piper also provides an open-source framework in which you can build a truly customized pipeline from many modules. Basically, such companies either do not provide an open-source framework, or provide a very truncated set of modules for experiments, which limits the freedom, functionality, and applicability of these platforms. This is partly similar to the hub of models and datasets in huggingface.
 
-#.
+#. Mlflow / DVC, etc. - There are also many excellent projects on the market for tracking experiments, serving and storing machine learning models. But they are increasingly utilitarian and do not directly help in the task of accelerating the construction of a machine learning MVP project. We plan to add integrations to Piper with the most popular frameworks for the needs of DS and ML specialists.
 
 
 Contributing
@@ -100,11 +84,11 @@ By submitting a pull request to this project, you agree to license your contribu
 
 
 
-.. |Banner| image:: https://tatradev.com
-   :target: https://tatradev.com
+.. |Banner| image:: https://static.tildacdn.com/tild3434-6665-4638-a432-626636353134/illistration.svg
+   :target: http://pipertool.org/
    :alt: Piper logo
 
 
 .. |Contribs| image:: https://tatradev.com
    :target: https://github.com/TatraDev/piper/graphs/contributors
-   :alt: Contributors
+   :alt: Contributors