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WHAT ARE THE REQUIREMENTS
FOR ENTERPRISE AI?
● Open-source (Linux, Hadoop)
● Scalable, Containerized, Fast
● Integrates With Existing Tech (JVM)
● Cross-Team Solution (DevOps, Data Science)
● General-Purpose, Customizable Framework
● ON PREMISE
● PUBLIC CLOUD
● HYBRID CLOUD
CONTAINERIZED DEEP LEARNING FOR ANY PLATFORM
DC/OS + SPARK
Intuitive Python API: Keras
Multi-GPUS Single GPU
Java API Scala API
JVM Big Data Stack:
Platform Neutral: On-prem/AWS..
General Purpose Deep Learning Platforms: For Image, Video, Sound, Text, Time Series Data
Enterprise distro certified on
CDH, HDP, Kerberos
R, Python, etc.
MAJOR DEEP LEARNING LIBRARIES COMPARED
FOUNDERS (YC W16)
Deep learning @GalvanizeU
• Author: O’Reilly’s “Deep learning: A
Practitioner’s Guide” Mar. 2016
• Speaker: Hadoop Summit, OSCon, Tech
• 3x startup founder
• CS/Biz @Michigan Tech
ADAM GIBSON, CTO CHRIS NICHOLSON, CEO
• As a recruiter: Helped triple team
through Series B to 45 staff
• As PR: Helped drive 45x rev. and AUM growth
($650M in June 2015)
• New York Times correspondent covering tech,
• Deep learning Systems Engineer
• Former Lightbend, Swisscom
• PhD in C.S. from l'Ecole Polytechnique
• Head Field Engineer
• ex-Principal Architect Cloudera
• O’Reilly Co-Author
• Author of JavaCPP (Cython for Java)
• PhD in Computer Vision from TIT
• Sr. ASIC Engineer at NVIDIA
• Physical Design Eng. H-P
• Doctoral Candidate (math)
• Java Software engineer
• NLP solutions builder
• GPU optimization
TELECOM CASE STUDY
FRANCE TELECOM'S MOBILE UNIT
Orange has a fraud problem costing it tens of millions of dollars a
year. It's called bypass fraud: bad actors on Orange's network avoid
paying international calling fees while still routing calls from one
country to another through VOIP. Orange's previous solution
needed days to detect a third-party service cheating on fees. In that
time, the actor could make tremendous profits. Orange's previous
system involved a combination of hard rules and SQL queries over
data stored on a Hadoop cluster.
Skymind's neural nets are able to detect anomalous calling patterns
that locate and identify bypass fraud with just a few hours' data.
Using Spark on top of Orange's Hadoop cluster, Skymind trained a
neural network architecture to detect unusual behavior that was
escalated to Orange's human analyst team, who then decide which
users to shut down. The results obtained are better than any
previous solution produced. The solution is now deployed on
Orange's servers in France.
A global bank processes payments from around the world and
needs to detect unusual behavior such as fraud and money
laundering. The bank had built its own solution which it maintained
and extended until it discovered Deeplearning4j. That solution was
brittle and rules based, and had trouble evolving quickly in an
adversarial situation. The process of developing that in-house
framework required the attention of rare feature engineers and
produced mediocre results.
Skymind's enterprise distribution of Deeplearning4j gave the bank
modular, well-maintained and hardware-optimized code which it
could extend to surface anomalous behavior. These filters were
integrated with data pipelines that included Kafka, Spark and HDFS,
and run on top of ND4J, a scientific computing library Skymind built,
which bridges academic hardware acceleration on GPUs with the
big data ecosystem of the JVM. The bank's engineers no longer had
to manually code their rules and filters, and were able to extend the
bank's infrastructure in other ways.
Enterprise clients pay Canonical to manage instances of
OpenStack, an open-source software platform deployed as
infrastructure as a service. OpenStack runs on top of commodity
hardware, and Canonical is responsible for its security and smooth
Skymind built DeepStack, a nervous system for OpenStack.
● Network intrusions through data packet inspections
● Imminent hardware breakdowns by analyzing server logs
DeepStack is used for cybersecurity and to rebalance workloads.
Bernie.ai is an innovative dating app and personal assistant. Bernie
needed a method to quickly analyze and cluster photographs of
faces in order to offer its users attractive potential dates based on
their likes and dislikes. More accurate recommendations reduce
dating fatigue. Existing deep-learning tools including well-known
Python frameworks Bernie experimented with were too slow.
Skymind's fast, accurate image recognition algorithms gave Bernie
a speed improvement of 3,700% and accuracy of more than 98% in