Earlier quoted context omitted.
> If you are building products / services that use ML and developing/training your own models (as opposed to taking pre-trained models and using them), there is really no credible competitor to TensorFlow. MXNet has amalgamation http://mxnet.io/how_to/smart_device.html#amalgamation-making... CNTK provides a managed ("evaluation") library solution to deploy your models and embed them in C, C++, C#, Python, and even an…
I also completely forgot about Caffe(2), which I recall to always have been the most easily deployable library, and possibly DL4J. http://www.cio.com/article/3193689/artificial-intelligence/w...
A lot of what we see is deployment to production. We are embedded in a few apache projects now as well as other "enterprise" suites like knime.
The reason for this is simplicity and integration with the JVM as well as supplemental addons for things like ETL (see: jdbc, hdfs, kafka,spark,..) as well as what I think is the easiest way to do both multi threaded model serving and data parallel training (parallelwrapper and parallelinfernece).
We also made things tunable from the JVM (eg: You can configure cuda, native cpu variables, blas libraries,..) from the JVM.
We import python models as well.
We aren't heavily used in the research world but are used at scale (especially in china).
Something we will have coming up is also the ability to import tensorflow models (right now we have keras 1 and need to add keras 2). The only thing we are missing and finishing out now (among other projects) is autodiff. We will have a lot of the same properties as the other "computation graph" frameworks like TF,theano,pytorch.
I'd also note we have a chainer/pytorch like cmpgraph api built in to our neural net dsl already.
Next release we will also have our new parameter server using aeron. Aeron is miles ahead of GRPC(https://github.com/benalexau/rpc-bench) being used in the low latency/quant world as well as being the default transport for akka now.