docs: Updated Mission Statement

README.md

@@ -9,26 +9,13 @@
 
 ## Mission Statement
 
-DFFML aims to be the easiest and most convenient way to use Machine Learning.
+As we all know the Machine Learning space has a lot of tools and libraries for creating pipelines to train, test & deploy models, and dealing with these many different APIs can be cumbersome.
 
-- Its a machine learning distribution. Providing you access to a set of popular
-  machine learning libraries guaranteed to work together.
+Our project aims to make this process a breeze by introducing interoperability under a modular and easily extensible API. DFFML’s plugin-based architecture makes it a swiss army knife of ML research & MLOps.
 
-- Its a AI/ML Python library, command line application, and HTTP service.
+We heavily rely on DataFlows, which are basically directed graphs. We are also working on a WebUI to make dataflows completely a drag’n drop experience. Currently, all of our functionalities are accessible through Python API, CLI, and HTTP APIs.
 
-- You give it your data and tell it what kind of model you want to train. It
-  creates a model for you.
-
-- If you want finer grained control over the model, you can easily do so by
-  implementing your own model plugin.
-
-- We make it easy to use and deploy your models.
-
-- We provide a directed graph concurrent execution environment with managed
-  locking which we call DataFlows.
-
-- DataFlows make it easy to generate datasets or modify existing datasets for
-  rapid iteration on feature engineering.
+We broadly have two types of audience here, one is Citizen Data Scientists and ML researchers, who’d probably use the WebUI to experiment and design models. MLOps people will deploy models and set up data processing pipelines via the HTTP/CLI/Python APIs.
 
 ## Documentation
 

docs/about.rst

@@ -24,26 +24,24 @@ include some sources, models, and operations as a part of the main package,
 Mission Statement
 -----------------
 
-DFFML aims to be the easiest and most convenient way to use Machine Learning.
+As we all know the Machine Learning space has a lot of tools and libraries for 
+creating pipelines to train, test & deploy models, and dealing with these many 
+different APIs can be cumbersome.
 
-- Its a machine learning distribution. Providing you access to a set of popular
-  machine learning libraries guaranteed to work together.
+Our project aims to make this process a breeze by introducing interoperability 
+under a modular and easily extensible API. DFFML’s plugin-based architecture makes 
+it a swiss army knife of ML research & MLOps.
 
-- Its a AI/ML Python library, command line application, and HTTP service.
+We heavily rely on DataFlows, which are basically directed graphs. We are also 
+working on a WebUI to make dataflows completely a drag’n drop experience. 
+Currently, all of our functionalities are accessible through Python API, CLI, 
+and HTTP APIs. 
 
-- You give it your data and tell it what kind of model you want to train. It
-  creates a model for you.
+We broadly have two types of audience here, one is Citizen Data Scientists and 
+ML researchers, who’d probably use the WebUI to experiment and design models. 
+MLOps people will deploy models and set up data processing pipelines via the 
+HTTP/CLI/Python APIs.
 
-- If you want finer grained control over the model, you can easily do so by
-  implementing your own model plugin.
-
-- We make it easy to use and deploy your models.
-
-- We provide a directed graph concurrent execution environment with managed
-  locking which we call DataFlows.
-
-- DataFlows make it easy to generate datasets or modify existing datasets for
-  rapid iteration on feature engineering.
 
 What is key objective of DataFlows
 ----------------------------------