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Self Guiding User Experience

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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

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Self Guiding User Experience

  1. 1. CONFIDENTIAL Venkatesh Yadav @venkateshai Sr. Director, Data Products & Applications Engineering July 19, 2016 Self Guiding User Experience
  2. 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. 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. 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. 5.  Predictive Feature Panel  Predictive Contextual Window What ?
  6. 6. 95 % Action 1 92 % Action 2 88 % Action 3 85 % Action 4 80 % Action 5 What ?
  7. 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. 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. 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 ?
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  11. 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. 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. 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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