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CONFIDENTIAL
Venkatesh Yadav @venkateshaiSr. Director, Data Products & Applications Engineering
July 19, 2016
Self Guiding User Experience
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
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
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
Predictive Feature Panel Predictive Contextual Window
What ?
95 % Action 1
92 % Action 2
88 % Action 3
85 % Action 4
80 % Action 5
What ?
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 ?
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 ?
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
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
REST API – H2O + Steam AI Engine
Training Dataset
Train Model
Dep
loy/
Sca
le
API Request
API RequestPrediction
Prediction
Data/Domain Scientist
Smart Apps
H2O
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
Domain ScientistsData Scientists
Software engineer Data Engineers
DevOps
14