aaronmarkham commented on a change in pull request #13657: update with release 
notes for 1.4.0 release
URL: https://github.com/apache/incubator-mxnet/pull/13657#discussion_r242236546
 
 

 ##########
 File path: NEWS.md
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 @@ -1,6 +1,565 @@
 MXNet Change Log
 ================
 
+## 1.4.0
+### New Features
+#### Java Inference API
+
+Model inference is run and managed by software engineers in a production 
eco-system which is built with tools and frameworks that use Java/Scala as a 
primary language. Inference on a trained model has two different use-cases:
+
+  1. Real time or Online Inference - tasks that require immediate feedback, 
such as fraud detection
+  2. Batch or Offline Inference - tasks that don't require immediate feedback, 
these are use-cases where you have massive amounts of data and want to run 
Inference or pre-compute inference results 
+Batch Inference is performed on big data platforms such as Spark using Scala 
or Java while Real time Inference is typically performed and deployed on 
popular web frameworks such as Tomcat, Netty, Jetty, etc. which use Java.  With 
this project, we want to build a new set of APIs which are Java friendly, 
compatible with Java 7+, are easy to use for inference, and lowers the entry 
barrier of consuming MXNet for production use-cases. More details can be found 
at the [Java Inference API 
document](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Java+Inference+API).
+
+#### Julia API 
+
+MXNet.jl is the Julia package of Apache MXNet. MXNet.jl brings flexible and 
efficient GPU computing and state-of-art deep learning to Julia. Some highlight 
of features include:
+
+  * Efficient tensor/matrix computation across multiple devices, including 
multiple CPUs, GPUs and distributed server nodes.
+  * Flexible symbolic manipulation to composite and construct state-of-the-art 
deep learning models.
 
 Review comment:
   Not sure if the grammar is intended based on the terms... how about this?
   ```suggestion
     * Flexible manipulation of symbolic to composite for construction of 
state-of-the-art deep learning models.
   ```

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