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 ?

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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

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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

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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

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