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SnapLogic Technology Open House – January 2018

Learn about some of SnapLogic's innovation projects and about the origins of Iris technology, which was originally just a research project.

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SnapLogic Technology Open House – January 2018

  1. 1. Cooking with AI/ML at SnapLogic SnapLogic Technology Open House | January 11, 2018 The Story of Iris
  2. 2. Education • Bachelor of Engineering in Computer Engineering @ Chulalongkorn University • Master of Science in Computer Science @ University of San Francisco Experience • Machine Learning Engineer @ SnapLogic Accomplishment • Published Research in Machine Learning • Fulbright Scholar • Cook Good Foods • Be Happy 2 Jump Thanawut Ananpiriyakul About Me
  3. 3. Agenda 3 Cooking with AI/ML at SnapLogic Get to Know SnapLogic • SnapLogic: Enterprise Integration Cloud Platform • Iris Artificial Intelligence: Machine-Learning-Driven Integration Assistant The Story of Iris • Iris's Restaurant • Survey the Market • Check our Inventory • Develop the Recipe The Future of Iris Jump's Dream
  4. 4. corporate overview Get to Know SnapLogic
  5. 5. 5 Get to Know SnapLogic Integration Platform as a Service (iPaaS) • Data Integration Platform • Connect Applications and Data • Self-Service, Fast, Easy • Reliable, Scalable • Accelerate Business
  6. 6. Our Product 6 Get to Know SnapLogic
  7. 7. 7 Get to Know SnapLogic Live Demo
  8. 8. Iris Artificial Intelligence 8 Get to Know SnapLogic What is Iris? • Artificial Intelligence • Friendly Assistant What is good about Iris? • Accelerate New Joiners • Improve Efficiency of Experts • Bring Happiness to Everyone
  9. 9. corporate overview The Story of Iris
  10. 10. Iris's Restaurant 10 The Story of Iris What kind of dishes should we serve here?
  11. 11. Survey the Market Check what people do. ❏ Research Papers ❏ Academic Proven ❏ Informative ❏ Developer Communities ❏ Full of Arguments ❏ Responsive ❏ Market Leaders ❏ Innovative ❏ Competitors 11 The Story of Iris What's good and how? ❏ Surgery Robot ❏ High Precision ❏ No Emotion ❏ No Fatigue ❏ Self Driving Car ❏ Less Accident ❏ Face ID ❏ More Secure Innovate new things. ❏ Exclusively for SnapLogic's Customers ❏ Useful & Reliable Estimate the cost. ❏ People ❏ Time ❏ Money
  12. 12. Check our Inventory 12 The Story of Iris Check what ingredients we have. ❏ Raw Data ❏ Log Data ❏ Traffic What are we going to cook? ❏ Recommendation Engine ❏ Pipeline Generator ❏ Pattern / Event Detector ❏ Automatic Optimizer ❏ Personal Assistant Chatbot ❏ Global Search Engine
  13. 13. Develop the Recipe 13 The Story of Iris Plating Make the Final Touch Turn off the heat. Add Thai chilies, kaffir lime leaves and lime juice. Make a quick stir. Pour the soup into the bowl. Serve with steamed Thai jasmine rice. Cooking Listen to the Voice of Ingredients Boil the water, add all herbs except Thai chilies and kaffir lime leaves. Wait until the onion is soft. Add mushrooms. Wait until boiled. Add Thai chili paste and shrimps. Wait until boiled. Season with fish sauce and sugar. Preparation Clean them All Wash lemongrass, galangal, Thai onion, Thai chilies and kaffir lime leaves. Let them dry, beat them with knife. Wash oyster mushrooms and straw mushrooms. Clean the shrimps, peel their shells, keep their heads. Serving Be Proud Serve while hot. Reheat if needed. Be careful when serving hot foods. Say "Please Enjoy!" with a big smile. Tom Yum Kung (ต้มยำกุ้ง) Thai River Prawn Spicy Soup
  14. 14. Develop the Recipe 14 The Story of Iris Preparation
  15. 15. Develop the Recipe 15 The Story of Iris Preparation Previous Snap 3 Previous Snap 2 Previous Snap 1 Current Snap Next Snap File Reader CSV Parser File Reader CSV Parser Mapper File Reader CSV Parser Mapper Copy File Reader CSV Parser Mapper Copy Mapper File Reader CSV Parser Mapper Copy Mapper CSV Parser Mapper Copy Mapper CSV Formatter CSV Parser Mapper Copy Mapper CSV Formatter
  16. 16. Develop the Recipe 16 The Story of Iris Preparation Dataset Extractor and Updater
  17. 17. Develop the Recipe 17 The Story of Iris Cooking Iris Development Process 3 RESULT ANALYSIS Analyze the result. Reverse engineer from result to data. Plan for improvement. 1 ALGORITHM DEVELOPMENT Choose the right algorithm. Choose the right configurations. Choose the right strategies. 2 EVALUATION Evaluate the algorithms using metrics: performance, accuracy, and cost.
  18. 18. Develop the Recipe 18 The Story of Iris Cooking Most Popular Snap Decision Tree
  19. 19. Plating Develop the Recipe 19 The Story of Iris Front-End UIBack-End Service Logging ServiceModel Builder
  20. 20. Serving Develop the Recipe 20 The Story of Iris
  21. 21. Develop the Recipe 21 The Story of Iris Plating Make the Final Touch Design and implement API on backend side. Design and implement user interface. Run load test to estimate the size of servers. Final test to make sure it is delicious. Cooking Listen to the Voice of Ingredients Try different cooking methods: statistical models, k-nearest neighbors, decision tree, random forests, etc. Evaluate with daily backtesting technique. Make sure the recommendations make sense. Preparation Clean them All Extract successfully executed pipelines. Transform into directed acyclic graphs. Chop them into segments of 5 nodes or less. Preprocess the data. Serving Be Proud Released in 4.9 (Spring, 2017). Source Snap Recommendation in 4.11 (Fall 2017). Observe usages and issues. Continue research and development.
  22. 22. corporate overview The Future of Iris
  23. 23. Neural Networks 23 The Future of Iris
  24. 24. Iris X Neural Networks 24 The Future of Iris Decision Tree ❏ It's interpretable. ❏ It takes minutes to train. ❏ It performs well on smaller dataset. ❏ It's solid and specific. ❏ It's for prototyping and early release. ❏ It's cheaper. Neural Networks ❏ It's magic. ❏ It takes hours or days to train. ❏ It's more powerful on large dataset. ❏ It's flexible and optimizable. ❏ It's for advancement and future release. ❏ It's a lot more expensive. Neural Networks and Deep Learning are definitely a present. They are powerful and super flexible. We are proud to announce that Iris has adopted those technologies.
  25. 25. The Truth 25 The Future of Iris Large Neural Networks (90%*) Expected to reach 90% hit rate with additional layers and wider layers. 1 Improved Decision Tree (81%) Prune the tree to get the best result.3 Statistical Model (49%) Always recommend top 5 popular snaps.5 Small Neural Networks (87%*) Look back upto last 9 snaps. Also learn from user information to make recommendations personalized. 2 Decision Tree (78%) Consider last 4 snaps on the canvas as an input set. Walk down the decision tree and get recommendations from the leaf. 4
  26. 26. Latest Prototype 26 The Future of Iris input_layer = Input(shape=(9,)) input_org_layer = Input(shape=(1,)) emb_layer = Embedding(num_snap_type, 128, input_length=seg_len-1)(input_layer) emb_org_layer = Embedding(num_org, 64, input_length=1)(input_org_layer) flatten_org = Flatten()(emb_org_layer) lstm_layer = LSTM(1024)(emb_layer) drop_layer = Dropout(0.2)(lstm_layer) merge_layer = Concatenate(axis=1)([drop_layer, flatten_org]) output_layer = Dense(num_snap_type, activation='softmax')(merge_layer) model = Model(inputs=[input_layer, input_org_layer], outputs=output_layer)
  27. 27. corporate overview Jump's Dream
  28. 28. Design Concept: The Easier Future of AI 28 Jump's Dream
  29. 29. Design Concept: The Easier Future of AI 29 Jump's Dream
  30. 30. Q & A Please Enjoy!Thank You for Coming