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Build vs. Buy Decisions in Data Science - Livestream

Many types of algorithms have become commoditized (NLP, Regression, AutoML), yet companies continue to use tight resources to try to build these in-house all the time. Considering that according to Gartner, nearly 9 out of 10 (87%) internal data science projects fail to make it into production, it's crazy to focus resources on anything but the most proprietary of projects. How do you decide where to focus?

Algorithm Commoditization: build vs. buy decisions in a resource crunched world

As a product 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 - in fact earlier this year there were 150k + unfilled jobs for data Scientists, and demand is only increasing.

Yet, companies are continually prioritizing their data scientists to build algorithms that have become commoditized.

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 - in fact earlier this year there was an estimate that there were 150k + unfilled jobs for data Scientists, and demand is only increasing. So given this, it is critical to know new technologies and understand what is truly proprietary to your organization versus a commodity.

New technologies like AutoML and the prevalence of well trained algorithms ranging from NLP to regression analytics 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. When you combine 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. Attendees will leave confident that they have a new process for deciding when to build versus buy an algorithm.

https://zoom.us/j/98108794573 Webinar ID: 981-0879-4573

Wednesday, 04/29/20

Contact:

Enes

Phone: +1(408)4754348
Website: Click to Visit

Cost:

Free

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