Understand the the chasm between AI research and real AI applications
Learn considerations for implementing AI product plans
Building an end-to-end AI application in production is tremendously more complicated than simply doing algorithm modeling in a lab. Simon Chan explains how to cross the gap between AI research fantasy into real-world applications. Drawing on his experience building the PredictionIO and Salesforce Einstein platforms, Simon details how enterprises navigate the end-to-end machine learning application development journey and shares the ongoing challenges that cloud-based AI dev platform providers face when they try to enable developers to build large-scale customized predictive applications in production environments.
In particular, Simon covers the 10 steps to bring AI into production:
Simon Chan is a senior director of product management for Salesforce Einstein, where he oversees platform development and delivers products that empower everyone to build smarter apps with Salesforce. Simon is a product innovator and serial entrepreneur with more than 14 years of global technology management experience in London, Hong Kong, Guangzhou, Beijing, and the Bay Area. Previously, Simon was the cofounder and CEO of PredictionIO, a leading open source machine learning server (acquired by Salesforce). Simon holds a BSE in computer science from the University of Michigan, Ann Arbor, and a PhD in machine learning from University College London.
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