The Commoditization of Algorithms

The Commoditization of Algorithms

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As a manager in charge of building data science based products, it is a constant challenge to prioritize the right things to build versus buy in a resource constrained environment. No one has unlimited data science talent. So given this, it is critical to know new technologies and understand what is truly proprietary to your organization versus a commodity.Technologies like AutoML and the prevalence of well trained algorithms have made it easier than ever to find accurate models, yet companies are continually prioritizing their data scientists to build algorithms that have become commoditized. Combined with this with the fact that Gartner estimates that 87% of data science projects never make it into production, it is an incredible economic waste.In this talk, Daniel Huss will walk through the best practices of product management when prioritizing which algorithms to build internally, as well as hi-light the latest technologies that are making many algorithms a commodity.

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