In this talk we will share the idea of developing self guiding application that would provide the most engaging user experience possible using crowd sourced knowledge on a mobile interface. We will discuss and share how historical usage data could be mined using machine learning to identify application usage patterns to generate probable next actions. #h2ony - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata

1. CONFIDENTIAL
Venkatesh Yadav @venkateshai
Sr. Director, Data Products & Applications Engineering
July 19, 2016
Self Guiding User Experience

2. In this talk we will share
- Idea of developing self guiding application that would
provide the most engaging user experience possible
using crowd sourced knowledge.
- Discuss and share how historical product usage data
could be mined using machine learning to identify
application usage patterns to generate probable next
actions.
Self Guiding User Experience

3. Why ?
 If an app takes more than a few seconds to learn, majority of users are
going to uninstall(Mobile)
 Creating that engaging, intuitive initial user experience is challenging,
predominantly constrained by
 Complexity of the application
 Screen real estate
 Domain knowledge, Familiarity
 Desktop/Web Experience with steep learning curve looses adoption

4. Why ?
 Mine user behavior patterns from crowd sourced application usage data.
 Identify High Value Actions/Workflows.
 Predict user’s next action based on current/previous actions.
 Provide best “Engagement Experience” possible.
 Focus on Experience beyond Algorithms and Data

5.  Predictive Feature Panel  Predictive Contextual Window
What ?

6. 95 % Action 1
92 % Action 2
88 % Action 3
85 % Action 4
80 % Action 5
What ?

7.  The Setting
 A mobile photo editing app.
 Relatively less complicated – approx. 20 possible actions
 Constrained in space – ribbon scroll and searching for actions
 The Goal
 Create engaging user experience, minimize scrolling and
searching
 Predictive Feature Panel and Contextual Window
What ?

8.  Crowdsourced Product Usage Data
 Each row is a set of actions (like a workflow) performed in an image editing session
 Total 100K rows of data, of approx. 20 possible actions
001 002 003
How ?

9.  Loose coupling between model creation and consumption
 Continuous model development and deployment capability
 Create Java POJO for the predictive model
 Provide REST API interface to predictive model
 Integration into an application
“Once models are deployed to the platform, they can
begin receiving API requests and sending predictions
back to the applications.”
How ?

10. 10

11. Automated Platform to Build and Scale Smart Data Products
Smart
Data
Product
Smart
Data
Product
Smart
Data
Product
AI – Machine Learning Automation Scalability Visual Intelligence
Smart
Data
Product
11
Dev Framework UX/UI Graphics
Tools, Logs,
Monitoring
Smart Data Product
Store
Smart
Data
Product
Smart
Data
Product
Smart
Data
Product
Smart
Data
Product
Smart
Data
Product

12. REST API – H2O + Steam AI Engine
Training
Dataset
Train Model
Deploy/Scale
 Data/Domain Scientist
 Smart Apps
Predictive
Model
(Java)
Predictive API
(Jar/WAR file)
Steam
Scoring
Servers
Steam Scoring
ServiceBuilder
Steam
Model
Manager
 Dev/Ops  Software/Data Engineer
Application Usage Data Collection

13. STEAM – Operationalize Data Science
• Single platform for DevOps, data scientists,
software engineers, and domain scientists to
collaborate on
• Support language of choice for different personas:
R, Python, Java
• Facilitate in-the-moment communication, reduce
model deployment time and get to the results much
faster
• Shared infrastructure with multi-tenancy support
• ElasticML to elastically manage and change the
size of underlying computing cluster
• Reduce your OPEX significantly
Improve Business Efficiency
Improve Operational Resource Efficiency
13
Domain ScientistsData Scientists
Software engineer Data Engineers
DevOps

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