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Chris Nicholson, CEO Skymind at The AI Conference

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Startup Showcase Presentation on Skymind

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Chris Nicholson, CEO Skymind at The AI Conference

  1. 1. THE WAY BUSINESS MAKES DECISIONS IS CHANGING ● Real-time Inference ● Evolving Statistical Models ● More Diverse Datasets
  2. 2. A SHIFT FROM BIG DATA TO MACHINE INTELLIGENCE Superhuman accuracy with machine perception
  3. 3. AI WILL IMPACT EVERY INDUSTRY
  4. 4. THE TOP TECH COMPANIES ARE POWERED BY DL ● Google ● Facebook ● Amazon ● Microsoft
  5. 5. THE REST WILL FOLLOW ... OR DIE
  6. 6. 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
  7. 7. SKYMIND GIVES BIG COMPANIES DEEP LEARNING
  8. 8. AN OPEN-CORE COMPANY CLOUDERA FOR AI ● Enterprise Distribution ● Easy Integration with Production Stack ● Supports Major Hardware ● ETL, Training, Inference for DL
  9. 9. AI-DRIVEN SOLUTIONS FOR ● BANKS ● TELCOS ● HEALTHCARE ● RETAIL ● MANUFACTURING ● DATA CENTERS
  10. 10. USE CASES ● FRAUD/ANOMALY DETECTION ○ BILLING, TELCO, ID ● IMAGE RECOGNITION ● PREDICTIVE ANALYTICS ○ MARKET FORECASTING ○ CHURN/TURNOVER ○ HARDWARE BREAKDOWNS
  11. 11. PRODUCT
  12. 12. WE BUILT DEEPLEARNING4J THE MOST WIDELY USED AI FRAMEWORK FOR JAVA
  13. 13. THE BRIDGE JOINING THE JVM PRODUCTION STACK & ACCELERATED HARDWARE SPARK JAVACPP FPGAARM Existing Planned
  14. 14. KEY INTEGRATIONS: SPARK, MESOS, KAFKA & HADOOP
  15. 15. FULL AI STACK: THE SKYMIND INTELLIGENCE LAYER (SKIL)
  16. 16. SKYMIND UI FOR DEPLOYMENT ON MESOS & DC/OS
  17. 17. ● ON PREMISE ● PUBLIC CLOUD ○ AWS ○ AZURE ○ GCE ● HYBRID CLOUD ● DESKTOP ○ WINDOWS ○ MAC ● ANDROID DOCKER CONTAINERIZED DEEP LEARNING FOR ANY PLATFORM DC/OS + SPARK MESOS
  18. 18. 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
  19. 19. MAJOR DEEP LEARNING LIBRARIES COMPARED
  20. 20. ROADMAP ● INTERPRETABLE MODELS ● MORE PRE-TRAINED MODELS ● VERTICAL SPECIFIC GUIs ● FEATURE PARITY WITH KERAS
  21. 21. PROOF
  22. 22. 100,000+ MONTHLY DOWNLOADS
  23. 23. 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
  24. 24. 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…)
  25. 25. 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)
  26. 26. KEY OPEN-SOURCE USERS • Cornell • Stanford • JPMorgan • IBM • U. Massachusetts • Yale • Harvard • MIT
  27. 27. OUR BOOK
  28. 28. 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)
  29. 29. TEAM
  30. 30. 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
  31. 31. 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
  32. 32. CASE STUDIES
  33. 33. TELECOM CASE STUDY FRANCE TELECOM'S MOBILE UNIT ORANGE
  34. 34. 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.
  35. 35. 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.
  36. 36. BANK CASE STUDY LARGE ASIAN BANK
  37. 37. 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.
  38. 38. 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.
  39. 39. DATA CENTER CASE STUDY CANONICAL
  40. 40. 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.
  41. 41. 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.
  42. 42. IMAGE RECOGNITION CASE STUDY BERNIE.AI
  43. 43. 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.
  44. 44. 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.
  45. 45. help@skymind.io

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