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

1. Cooking with AI/ML at SnapLogic
SnapLogic Technology Open House | January 11, 2018
The Story of Iris

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. 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. corporate overview
Get to Know SnapLogic

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. Our Product
6
Get to Know SnapLogic

7. 7
Get to Know SnapLogic
Live Demo

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. corporate overview
The Story of Iris

10. Iris's Restaurant
10
The Story of Iris
What kind of dishes should we serve here?

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. 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. 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. Develop the Recipe
14
The Story of Iris
Preparation

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. Develop the Recipe
16
The Story of Iris
Preparation
Dataset Extractor and Updater

17. Develop the Recipe
17
The Story of Iris
Cooking
https://www.bangkokpost.com/lifestyle/social-and-lifestyle/1381587/a-reluctant-star
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. Develop the Recipe
18
The Story of Iris
Cooking
Most Popular Snap Decision Tree

19. Plating
Develop the Recipe
19
The Story of Iris
Front-End UIBack-End Service Logging ServiceModel Builder

20. Serving
Develop the Recipe
20
The Story of Iris

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. corporate overview
The Future of Iris

23. Neural Networks
23
The Future of Iris
http://playground.tensorflow.org

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. 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. 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. corporate overview
Jump's Dream

28. Design Concept: The Easier Future of AI
28
Jump's Dream

29. Design Concept: The Easier Future of AI
29
Jump's Dream

30. Q & A
Please Enjoy!Thank You for Coming