Case Study: Building a Production Recommendation Engine on Accumulo

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Use Case


We all know that AI and Machine Learning (ML) are the new hotness in the big data space. There is plenty of focus on fancy ML algorithms, but what is really required to put something like a Recommendation Engine into production? The truth is, it requires a hefty amount of data engineering and data science. During this talk, attendees will step through a real-life case study on putting a Recommendation Engine into production and learn why Accumulo was an ideal choice for use in this particular situation.


Gadalia O'Bryan
Director of Delivery, Koverse

Gadalia has spent her career delivering robust, repeatable software solutions with real business and mission value. She enjoys partnering with customers to rapidly move forward and see a return on investment from their big data implementations.

Gadalia worked for the National Security Agency (NSA) for nearly a decade before leaving the public sector to join Koverse. At the NSA, Gadalia built production big data solutions that are referenced by programs throughout the U.S. Department of Defense and Intelligence Community. She also led large-scale security evaluations of cryptographic algorithms and information security systems, including both commercial and government applications.

Gadalia holds a Bachelor of Arts (BA) degree in Mathematics from the University of Colorado at Boulder, where she published work in the journal Numerical Algorithms. She holds a Master of Arts (MA) degree in Mathematics from UCLA, specializing in Algebraic Topology.