"Developing a new product nowadays can be a challenge. A service cannot survive without supporting multiple platforms, having a smooth user experience, and providing features that just work. During this presentation, you can see how such a prototype was built in 10 days, rethinking best practices and introducing a novel approach for the restaurant suggestion. A number of technologies and platforms are covered, including service-side development with NodeJS, iOS and Android development with React-Native, and image processing with TensorFlow."

1. Oct.28.2017
Steve, Cedric, Liboon
Rakuten, Inc.

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Stevche Radevski
Msc Software Engineering
Working on internal business
support/analytics tools.
Mainly working with JavaScript
(React, React Native, NodeJS)
Cedric Konan
Msc Ubiquitous Computing
Working on Dining services
tools.
Mainly working with Java but
a php lover
Tan Li Boon
Bsc Computer Science
Working on big data with
dev-ops.
Mainly working with Java
(Spark, Couchbase, Hadoop)

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Given 3 months to do anything we want!
Every Friday.
Any topic.
Work however we want.
FREEDOM!

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Play around with Machine Learning
Do something differently
Utilize everyone’s specialized skills and learn from each other
Multiplatform mobile app development

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…

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Fast Development
Multi-platform
Easily-interfaceable
Plug and play

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A JavaScript based framework to build native, multi-platform mobile applications
1487 official contributors on GitHub
1000+ mobile apps created with it

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Easy to use (especially if you know React)
Based on JavaScript
Uses markup language syntax
Reusable Components
Very well supported (documentation, active community)

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App Result list
Star Rating Component
Row component
List View

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Recognize what a restaurant sells based on their
food pictures!

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No billing surprises
Demonstration of expertise
Specialized Models
A technology company should maintain in-house infrastructure

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References:
Yoshiyuki Kawano and Keiji Yanai,
The University of Electro-
Communications, Tokyo, Japan
256 food categories, 100
images per category
UEC FOOD-256

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Inception-v3 is a convolution-based neural network (ConvNet).
It takes 2 to 3 weeks on multiple GPUs to train a ConvNet from zero!
If you need to tweak the network parameters, you have to re-train the whole thing.
Clearly this is not reasonable.
Enter Transfer Learning…

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Use the outputs of another trained network as generic image feature detectors,
and train a new shallow model using these outputs.
softmax
conv2
conv1
Images and tags
loss
softmax
conv2
conv1
Images and tags
loss
Original Target
Pre-trained

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Google Vision API
Mine Restaurants Data
Backend API
Vision API
WannaEat Vision
Restaurants.json
Get Tags Per Image
Per Restaurant
Image – Tag
Dictionary
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Store reduced
tags per restaurant
Get 1000 restaurants from Google Maps
Generate tags list
per image

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Mobile
Google Vision API
Backend API
Vision API
WannaEat Vision
Restaurants.json
User uploaded Image
Image or Tag
Top tag
for image
Matching
Restaurants

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Collocation is great (no communication friction)
Freedom: Possibility to choose topic and tech stack.
Small team, so no need for long meetings, tickets, wikis (Trello and p2p talk).
Clear system boundaries with clear interfaces between each boundary.
Well-defined responsibilities (but still helping each other)

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Nothing, we are that good!

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Took some time to remember what we did the last week
Collecting data for restaurants was time-consuming
Vision processing took equally long.
Spent half the time to figure out what problem to tackle

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Of course we did!
Work in small teams!
Have clearly defined responsibilities
Do one thing at a time (switching between projects takes mental effort)
Machine Learning is not difficult anymore (many API as a service providers)
Rethink best practices (both development and UI/UX)