How to build successful machine learning products (that solve real problems and make money!)
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Once considered to be niche technologies limited to the domain of academic research and the few large global tech companies, today machine learning and AI are finding innovative application in almost every industry by companies of every size. While there have been remarkable success stories of machine learning products which have disrupted entire industries, there have also been a large number of attempts to build machine learning based solutions that have failed miserably. Analytics products are unique relative to traditional hardware or software products in the high level of technical uncertainty and risk often involved. What makes an analytics product successful goes beyond the quality of the algorithm developed; it also includes the understanding of the customer’s needs, the availability of data, and the team’s discipline in following the data science process. Join our host for this session Jon Reifschneider as he shares lessons learned from building predictive analytics products now in use by most of the major US electric utilities and airlines around the world on the importance of data, process, talent and customer engagement alongside technical approach.
Before the session, all registrants will receive an e-mail with a link and meeting information.
|Date||Wednesday, November 4th, 2020|
|Time||4:30pm - 6:00pm|