Update README.md

README.md

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-hyperflow is a Machine learning platform for growing teams. The platform adds reusuability and scalability to your machine learning stack. It does by componentizing model development and executing training tasks inside containers. 
+**hyperflow** is a Machine learning platform for growing teams. The platform adds reusuability and scalability to your machine learning stack. It does by componentizing model development and executing training tasks inside containers.
 
 You can use the current version of hflow just keeping track of model-experiment cycle or also utilize containerized (docker) training and automated version control when deployed with kubernetes.
 
 The project is in alpha stage but if you are keen to try out do write us at beekeepr@hyperflow.cc
 
 ![Alt Text](http://www.animatedgif.net/underconstruction/cns01_e0.gif)
 
-# Key Features:
+### Key Features:
 
 * Reduced experimentation time: Componentization and parallel training on cloud to optimise time to experiment. Don't spend weeks to experiment when you can finish in days.
 * Ease-of-use: Launch tasks (e.g training, data processing) with a single command
@@ -19,13 +19,17 @@ The project is in alpha stage but if you are keen to try out do write us at beek
 * Framework Support: Tensorflow, Keras, pytorch out of box. You can add your own as a docker image
 * GPU Support
 
-A typical workflow with hf will look like: 
+### Documentation
+https://hyperflow.in/docs/introduction
 
-##### Initiate repo
+### Example
+A typical workflow with hflow will look like: 
+
+##### 1. Initiate repo
 $ hflow init my_repo <br />
 > Repo my_repo initiated
 
-##### upload dataset (optional) 
+##### 2. upload dataset (optional) 
 $ cd /home/data  <br />
 > hflow init my_dataset  <br />
 > hflow push <br />