2. THE WAY BUSINESS MAKES
DECISIONS IS CHANGING
● Real-time Inference
● Evolving Statistical Models
● More Diverse Datasets

3. A SHIFT FROM BIG DATA
TO MACHINE INTELLIGENCE
Superhuman accuracy
with machine perception

4. AI WILL IMPACT EVERY INDUSTRY

5. THE TOP TECH COMPANIES
ARE POWERED BY DL
● Google
● Facebook
● Amazon
● Microsoft

6. THE REST WILL FOLLOW ... OR DIE

7. 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

8. SKYMIND GIVES
BIG COMPANIES
DEEP LEARNING

9. AN OPEN-CORE COMPANY
CLOUDERA FOR AI
● Enterprise Distribution
● Easy Integration with Production Stack
● Supports Major Hardware
● ETL, Training, Inference for DL

10. AI-DRIVEN SOLUTIONS FOR
● BANKS
● TELCOS
● HEALTHCARE
● RETAIL
● MANUFACTURING
● DATA CENTERS

11. USE CASES
● FRAUD/ANOMALY DETECTION
○ BILLING, TELCO, ID
● IMAGE RECOGNITION
● PREDICTIVE ANALYTICS
○ MARKET FORECASTING
○ CHURN/TURNOVER
○ HARDWARE BREAKDOWNS

12. PRODUCT

13. WE BUILT
DEEPLEARNING4J
THE MOST WIDELY USED AI
FRAMEWORK FOR JAVA

14. THE BRIDGE JOINING
THE JVM PRODUCTION STACK
& ACCELERATED HARDWARE
SPARK
JAVACPP
FPGAARM
Existing Planned

15. KEY INTEGRATIONS: SPARK, MESOS, KAFKA & HADOOP

16. FULL AI STACK: THE SKYMIND INTELLIGENCE LAYER (SKIL)

19. SKYMIND UI FOR DEPLOYMENT ON MESOS & DC/OS

20. ● ON PREMISE
● PUBLIC CLOUD
○ AWS
○ AZURE
○ GCE
● HYBRID CLOUD
● DESKTOP
○ WINDOWS
○ MAC
● ANDROID
DOCKER
CONTAINERIZED DEEP LEARNING FOR ANY PLATFORM
DC/OS + SPARK
MESOS

22. DeepLearning4j TheanoTensorFlow
Intuitive Python API: Keras
Multi-GPUS Single GPU
Java API Scala API
JVM Big Data Stack:
Hadoop, Spark,
Kafka, ElasticSearch
CaffeTorch
Lua API
Image Only
Commercial Support
Platform Neutral: On-prem/AWS..
Optimized for
Google Cloud
& TPUs
General Purpose Deep Learning Platforms: For Image, Video, Sound, Text, Time Series Data
Python API
Multi-GPUS
Enterprise distro certified on
CDH, HDP, Kerberos
MxNet
R, Python, etc.
MAJOR DEEP LEARNING LIBRARIES COMPARED

23. MAJOR DEEP LEARNING LIBRARIES COMPARED

24. ROADMAP
● INTERPRETABLE MODELS
● MORE PRE-TRAINED MODELS
● VERTICAL SPECIFIC GUIs
● FEATURE PARITY WITH KERAS

25. PROOF

26. 100,000+ MONTHLY DOWNLOADS

27. TRACTION
TRAFFIC
• 160,000+ Web site Hits per Month
• SEO: No. 1 “deep learning java”, “deep learning
open source” …
DOWNLOADS
• 100,000+ downloads/mo
• 20% mo/mo growth in downloads
PUBLICITY
• Wired, TechCrunch, WSJ, Bloomberg, VB

28. TRACTION 2
Skymind Projects on Github
• 5,000 stars; 2000+ forks
• Top 0.001% of all Java projects
• Comparisons
• Mesos: ~2,700 stars
• Reported $1B valuation
• Impala : ~1,700 stars
• (Supported by Cloudera,
a $4.1B enterprise co.
with $1.2B in funding…)

29. GO-TO-MARKET PARTNERS
• Nvidia: Co-promotion, joint customers, Inception
• Intel: Deep-Learning Partner (MOU)
• Cloudera: Certified, co-promotion
• Hortonworks: Certified, DL4J apps built
• IBM: Customer. Multiple teams, uses. Watson IoT
• Lightbend (Lagom for micro-services)

30. KEY OPEN-SOURCE USERS
• Cornell
• Stanford
• JPMorgan
• IBM
• U. Massachusetts
• Yale
• Harvard
• MIT

31. OUR BOOK

32. INVESTORS
• SV ANGEL (RON CONWAY)
• GPV (ORACLE CEO RAY LANE)
• Y COMBINATOR
• TENCENT
• MANDRA CAPITAL
• RISING TIDE FUND
• LIQUID 2 VENTURES
• AMIT SINGHAL (GOOGLE SEARCH)
• KRISHNA BHARAT (GOOG. NEWS)
• KEVIN MAHAFFEY (LOOKOUT)

33. TEAM

34. FOUNDERS (YC W16)
Deep learning @GalvanizeU
• Author: O’Reilly’s “Deep learning: A
Practitioner’s Guide” Mar. 2016
• Speaker: Hadoop Summit, OSCon, Tech
Planet, GigaOM
• 3x startup founder
• CS/Biz @Michigan Tech
ADAM GIBSON, CTO CHRIS NICHOLSON, CEO
Sequoia’s FutureAdvisor
• 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,
M&A: 2006-2011

35. SKYMIND ENGINEERS
FRANCOIS GARILLOT
• Deep learning Systems Engineer
• Former Lightbend, Swisscom
• PhD in C.S. from l'Ecole Polytechnique
JOSH PATTERSON
• Head Field Engineer
• ex-Principal Architect Cloudera
• O’Reilly Co-Author
SAMUEL AUDET
• Author of JavaCPP (Cython for Java)
• PhD in Computer Vision from TIT
SUSAN ERALY
• Sr. ASIC Engineer at NVIDIA
• Physical Design Eng. H-P
ALEX BLACK
• Doctoral Candidate (math)
• Java Software engineer
V. KOKORIN
• NLP solutions builder
• GPU optimization

36. CASE STUDIES

37. TELECOM CASE STUDY
FRANCE TELECOM'S MOBILE UNIT
ORANGE

38. PROBLEM
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.

39. SOLUTION
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.

40. BANK CASE STUDY
LARGE ASIAN BANK

41. PROBLEM
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.

42. SOLUTION
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.

43. DATA CENTER CASE
STUDY
CANONICAL

44. PROBLEM
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
operation.

45. SOLUTION
Skymind built DeepStack, a nervous system for OpenStack.
DeepStack detects:
● Network intrusions through data packet inspections
● Imminent hardware breakdowns by analyzing server logs
DeepStack is used for cybersecurity and to rebalance workloads.

47. IMAGE RECOGNITION
CASE STUDY
BERNIE.AI

48. PROBLEM
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.

49. SOLUTION
Skymind's fast, accurate image recognition algorithms gave Bernie
a speed improvement of 3,700% and accuracy of more than 98% in
facial recognition.

51. help@skymind.io