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Using Neo4j and Machine Learning to Create a Decision Engine, CluedIn

GraphConnect Europe 2017
Tim Ward, CluedIn

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Using Neo4j and Machine Learning to Create a Decision Engine, CluedIn

  1. 1. Taking the "Magic" Out of Machine Learning Building a decision engine with Neo4j and Machine Learning Techniques
  2. 2. Tim Ward Engineer at CluedIn @jerrong / tiw@cluedin.com Using Neo4j for 5+ years Started on 1.6 WHO AM I ?
  3. 3. WHAT DO WE DO? We help our customers achieve the connected enterprise Average company uses 30+ SAAS tools We connect them and the data in them automatically (SAAS)
  4. 4. WHY “MAGIC” Like everyone, when we heard there are new techniques called "Machine Learning", we jumped on early and learnt.
  5. 5. STARTED OUT SMALL 92% Suit 5% Bow Tie 2% Penguin 1% Other
  6. 6. FAILED QUICKLY 97% Fur Coat 2% Bucket 1% Other
  7. 7. MOVED IN SMALL STEPS Clustering Neural Networks
  8. 8. DISCOVERED The thing you learn through all of this is that machine learning techniques are good at solving certain problems, not the magic bullet for all problems.
  9. 9. THE SIMPLE IDEA Have a weighted decision engine that can persist Have the ability to fork graph decisions async Does not need to be super fast or realtime Get something from nothing
  10. 10. WHY To disseminate noise from valuable data To reverse engineer how two things are related To connect the enterprise.....automatically
  11. 11. THE SIMPLE APPROACHES CAN GET YOU VERY FAR! We combine the best parts of the graph with the backing of a neural network to learn from its decisions. Pattern matching combined with statistical models.
  12. 12. OUR PRACTICAL PROCESS Recursive Decision Tree that organically grows, expands, collapses then learns.
  13. 13. PRE-PROCESSING PIPELINE We combine the best parts of the graph with the backing of a neural network to learn from its decisions. Pattern matching combined with statistical models.
  14. 14. Martin Hyldahl, CTO “The graph is the new secret in machine learning as most models are dots on a chart or rows in a model. Besides clustering algorithms there are not a lot of algorithms where the dots are related in a strong and meaningful way. Although this typically requires a lot more processing, we found that this tapers off over time. The pre-processing that we do to get data into a connected graph before we make the decision tree allows our engine to be statistically correct more than any known approach today.”
  15. 15. LET'S SEE IT IN ACTION Who is Emil Eifrém?
  16. 16. SOMETHING HARDER What is the best way for me to contact Emil Eifrém of Neo Technologies?
  17. 17. SOMETHING "MAGIC" How do I sell to Neo Technologies?
  18. 18. HOW DID WE COME UP WITH THE WEIGHTS? The weights are constantly being re-evaluated.
  19. 19. MACHINE LEARNING SUPERVISED "Is Emil still the CEO of Neo?" UNSUPERVISED Neural Network built from statistical decisions
  20. 20. GOOD PARTS Building the graphs are easy, they are less of a black box. Crossed Path Intersection Count.
  21. 21. CHALLENGING PARTS You can't do this real-time in the graph....turns out this actually helps
  22. 22. SOMETHING OUT OF NOTHING Because typically you have more than nothing
  23. 23. SO WHY DO ALL OF THIS? Connected data is always more interesting that disconnected data.
  24. 24. HOW CAN YOU START USING THIS TECHNIQUE? cluedin.com/developers to request an API key.
  25. 25. WHY FOR ENGINEERS? Data Cleansing Polyglot Persistence Enrich data Machine Learning
  26. 26. WHY FOR THE BUSINESS? Talk to Amalie (ale@cluedin.com) Right to be forgotten Data Privacy Act cluedin.com/sales

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