H2O World 2015 - Venkatesh Yadav of Adobe

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Self Guiding Applications
Venkatesh Yadav

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Why ?
 If an app takes more than a few seconds to learn, a majority of users are going to
uninstall (MOBILE).
 Creating that engaging initial use experience is challenging, predominantly
constrained by
 Complexity of the app
 Screen space
 Etc.,
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Why ?
 We want to provide best “Engagement Experience” possible using crowd sourced
application usage data.
3

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Self Guiding Applications
4

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Self Guiding Applications
95
%
Action 1
92
%
Action 2
88
%
Action 3
85
%
Action 4
80
%
Action 5

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Phase 1 – Proof Of Concept
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 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

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Crowdsourced Product Usage Data
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 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

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Machine Learning Algorithms
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 Markov Model
 Next action is dependent only on the present action, not on the sequence of previous actions
 N Action Bayesian Model
 Next action is dependent on the sequence of past N actions
 N Action Neural Network Model
 Next action is dependent on the sequence of past N actions

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Performance
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Predictive APIs
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 Evaluated few approaches to expose predictive models via HTTP REST
APIs
 Loose coupling between model creation and consumption
 Continuous model development and deployment capability
 Create Java POJO for the predictive
model
 Wrap POJO by any Java Web API
framework
 Can better integrate with Adobe.io
platform

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REST API – H2O
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Training
Dataset
Platfor
m
Server
Train Model
 Data Science
Team
 Smart Apps
Predictiv
e Model
(Java)
Predictive
API (Jar
file)

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Demo
“Once models are deployed to the platform, they can
begin receiving API requests and sending predictions back
to the applications.”
 Loose coupling
 Continues model development
 Provide REST API interface to predictive model
(Demo)
 Integration into an application (Next Step)

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Demo