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Building an AI in the Cloud
Recorded at:

| by Simon Chan Follow 0 Followers on Oct 02, 2016 |
44:58

Summary
Simon Chan shares the on-going challenges ML SaaS platforms face when developers try to build customized large-scale predictive applications on production environments. He talks about the ML platform product design dilemma, the difference between building A.I. in a lab and on production, and the steps to build an end-to-end Machine Learning solution, from data sourcing to live monitoring etc.

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Bio

Simon Chan is a serial entrepreneur and product innovator. Recently, he joined Salesforce as a Senior Director. He was the founding CEO of PredictionIO - an open source Machine Learning Server that empowers programmers and data engineers to build smart applications. PredictionIO has become the most popular Spark-based machine learning project on Github.

Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams.

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