Skymind is an enterprise AI platform that offers the Skymind Intelligence Layer (SKIL), a commercial distribution of machine learning tools including libraries for neural network training and data processing. Founded in 2014, the company has raised $6.3M and serves 12 enterprise clients while supporting over 160,000 downloads of their deep learning library. Upcoming features include enhancements for their Python interface and improved integration with streaming technologies like Kafka.

2. What Is Skymind?
● Skymind is Red Hat for AI.
● The Skymind Intelligence Layer (SKIL) is RHEL:
An enterprise distribution backed by
commercial support.
● SKIL bundles libraries that Skymind built:
○ Deeplearning4j: Neural net configuration
○ ND4J: Scientific computing engine
○ DataVec: ETL tool for machine learning
○ Deep-learning model server w/REST API
● SKIL helps you train neural nets quickly and
get the maximum value from your hardware

3. Founded 2014
Funding $6.3M
Sales $1M+ in Q1 2017
$4-6M projected in 2017
Clients 12 enterprise clients
10,000+ open-source developers
160,000+ DL4J downloads/mo.
Staff 25
Company Overview

4. Reference Architecture

5. DL4J
Build, train, and deploy neural
networks on the JVM
RL4J
Reinforcement learning
algorithms on the JVM
ND4J
High-performance tensor library
for scientific computing
Skymind’s Open-Source Tools
Arbiter
Hyperparameter optimization for
neural networks
DataVec
Data ingestion, normalization, and
vectorization (ETL for ML)
Model Import
Import and deploy neural networks
trained from Keras, TensorFlow &
Theano

6. ParallelWrapper
● Single node parameter Averaging
● Use a datasetiterator but run multigpu
or just use all cores
● Also useful for testing parameter averaging on a single
node before going distributed
https://github.com/deeplearning4j/dl4j-examples/blob/maste
r/dl4j-cuda-specific-examples/src/main/java/org/deeplearnin
g4j/examples/multigpu/MultiGpuLenetMnistExample.java#L40

7. Datavec TransformProcess
● Persistable data pipelines
● Mainly useful for csv and log data right now
● Also comes with a transform process server for encasing
data transform as a service
● https://github.com/deeplearning4j/dl4j-examples/blob/mas
ter/datavec-examples/src/main/java/org/datavec/transfor
m/join/JoinExample.java

8. Nd4j indexing
● Use the static methods in NDArrayIndex
● Allows slicing of an array any way you could in numpy
● Boolean indexing also supports masking
● https://github.com/deeplearning4j/dl4j-examples/blob/mas
ter/nd4j-examples/src/main/java/org/nd4j/examples/Nd4jE
x6_BooleanIndexing.java

9. Arbiter
● Define a search space
● Supports Grid and Random search
● Comes with a GUI for looking at an overall search space
● https://github.com/deeplearning4j/dl4j-examples/tree/mas
ter/arbiter-examples

10. Nearest neighbors!
● We have VPTrees, QuadTrees,KDTrees,SPTrees
● Used in BarnesHutTsne and our nearest neighbors server
● Combined with an autoencoder variant it’s also a great way
of leveraging unsupervised algorithms (run nearest
neighbors on the representations from the neural net

11. Libnd4j
● Self contained c++ library
● Actually contains cuda kernels and openmp loops
for various types of algorithms (reductions,scans,..)
● Also contains bindings for blas and lapack
● https://github.com/deeplearning4j/libnd4j

12. Cudnn
● Dl4j actually comes with cudnn
● All you have to do is include it as a dependency
● We support lstms and the cnns
● It’s actually possible to use this on flink (just tricky to
setup)

13. ParallelInferece
● Thread pool oriented model serving
● Supports keras and dl4j models
● Useful for saturating your server also gets around
models not being thread safe
● Works similar to parallelwrapper

14. Nd4j Workspaces
● Our new cyclic memory management engine
● Allows you to turn off garbage collection
● We have seen a 3 to 5x speed improvement using it
● http://deeplearning4j.org/workspaces

15. Upcoming features
● Jumpy (our python interface) -
https://github.com/deeplearning4j/jumpy
● Autodiff
● Integration of our aeron based parameter server
● proper keras backend

16. Dl4j streaming
● Integrate with kafka and setup streaming jobs
● Needs some work and promotion but is a great way of
leveraging kafka for production
● Also supports spark streaming
● Flink contributions welcome

17. Transfer Learning
● Take pretrained models from keras and load them in to
dl4j and write a “fine tune configuration”
● Use this to build new image models quickly
● Use our model zoo and refine existing models

18. Nd4j Workspaces
● Our new cyclic memory management engine
● Allows you to turn off garbage collection
● We have seen a 3 to 5x speed improvement using it
● http://deeplearning4j.org/workspaces

19. Community showcase
● Apache opennlp is integrating arbiter and our LSTMS
● Apache Tika recently merged our
● Apache Flink wants to merge us for GPU support
● Tons of traction with the spring boot community