diff --git a/tech-list/modules/ROOT/pages/index.adoc b/tech-list/modules/ROOT/pages/index.adoc
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@@ -214,7 +214,7 @@ https://reagent.ai/[pytorch - Reinforcement Learning Platform^]
 |PyTorch is open source deep learning framework created by developers at Facebook and released in 2017. 
 |TensorFlow is open source deep learning framework created by developers at Google and released in 2015. 
 
-|Top PyTorch Projects 
+|*Top PyTorch Projects*
 
 CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. https://stanfordmlgroup.github.io/projects/chexnet/[url^]
 
@@ -222,7 +222,7 @@ PYRO: Pyro is a universal probabilistic programming language (PPL) written in Py
 
 Horizon: A platform for applied reinforcement learning (Applied RL) https://horizonrl.com[url^]
 
-|Top TensorFlow Projects
+|*Top TensorFlow Projects*
 
 Magenta: An open source research project exploring the role of machine learning as a tool in the creative process. https://magenta.tensorflow.org/[url^]
 
@@ -230,6 +230,46 @@ Sonnet: Sonnet is a library built on top of TensorFlow for building complex neur
 
 Ludwig: Ludwig is a toolbox to train and test deep learning models without the need to write code. https://uber.github.io/ludwig/[url^]
 
+
+| *Production Deployment*
+PyTorch doesn't provide any framework to deploy models directly on to the web. You'll have to use either Flask or Django as the backend server.
+| *Production Deployment*
+When it comes to deploying trained models to production, TensorFlow is the clear winner. We can directly deploy models in TensorFlow using TensorFlow serving which is a framework that uses REST Client API.
+
+
+|*Visualization*
+PyTorch developers use Visdom, the features provided by Visdom are very minimalistic and limited.
+|*Visualization*
+When it comes to visualization of the training process, TensorFlow takes the lead. Visualization helps the developer track the training process and debug in a more convenient way. 
+TenforFlow’s visualization library is called TensorBoard. 
+
+|Python
+|Tensorflow has APIs for various langages :
+
+*     C (used to develop other APIs in practice, though)
+*     C++ (Tensorflow is written in C++ and CUDA)
+*     Java
+*     GO
+*     Rust
+*     Haskell (yes, even Haskell)
+*     C#
+*     Julia
+*     R
+*     Python
+* 
+* And, of course, for JavaScript, since 2019.
+
+|*Pytorch Cons:*
+
+*    Third-party needed for visualization.
+*    API server needed for production.
+
+|*TensorFlow Cons:*
+
+*    Debugging method.
+*    Hard to make quick changes.
+
+
 |===