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Data By the Bay is the first Data Grid conference matrix with 6 vertical application areas  spanned by multiple horizontal data pipelines, platforms, and algorithms.  We are unifying data science and data engineering, showing what really works to run businesses at scale.
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Thursday, May 19 • 9:50am - 10:30am
Building an A.I. Cloudy Sky: What We Learned at PredictionIO

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Building a successful A.I. cloud platform on top of an open-source Machine Learning project is more complicated than one would imagine. Simply offering a hosted version of the project is hardly the answer. The secret to success is to differentiate the needs between the open-source users and the potential SaaS users. Oftentimes, they are of different species. We will walk through how PredictionIO navigates the roadmap. Building a software-as-a-service business on top of an open-source project involves more than just providing a hosted version of it. It is a little-known fact that people in the existing open-source user segment are unlikely to become the potential cloud product customers. To productize an open-source project as a cloud platform, understanding who the customers are and what they are willing to pay for are the keys. Business techniques like customer discovery and lean product development can be applied to increase the chance of success. At the same time, keeping the open-source project and the cloud product under one umbrella while developing them separately for different user segments is non-trivial. We'll dive into some real scenarios, both successful and not so successful ones, of how we navigate the cloud product roadmap based on the popular open-source Machine Learning server project -- PredictionIO. Specifically, we will walk through these topics: • Evaluating Different Cloud Approaches • Understanding Existing and Potential Users • Finding the Unique Values of the Cloud • Contributing Back From the Cloud to the Open-Source

Speakers
avatar for Simon Chan

Simon Chan

CEO, PredictionIO
Simon Chan is a co-founder of PredictionIO, with years of experience in the tech industry in London, Hong Kong, Mainland China and Silicon Valley. His doctoral research work at University College London was on machine learning techniques for large-scale user preference prediction in noisy non-experimental environments.



Thursday May 19, 2016 9:50am - 10:30am
Markov

Attendees (10)