Our new paper: “Columnar Database Techniques for Creating AI Features”
Brad Carlile, Akiko Marti & Guy Delamarter
http://arxiv.org/abs/1712.02882 (upcoming conference submission)
(special thanks to Brian Whitney & Paul Kinney for their assistance).
In this paper we show:
- Our new innovation to improve the performance of columnar databases (NoSQL & RDBMS) for the purpose of AI featurization (ML & DL).
- Our additions to columnar database dictionaries enable efficient feature creation calculations, by halving the required data movement and optimizing the compute.
- We propose fully integrated DB+AI architecture rich with information flows and learning feedback mechanisms that would further improve the whole analytics cycle. We point out other innovations in this area..
- We also give a survey of the various techniques so that subject matter experts in the diverse fields of Database, Machine Learning, and Deep Learning can understand about critical aspects that are outside their expertise.
This innovation came about by using real Performance Engineering skills.
- We have a cross-stack understanding AI full stack (Datasource to AI). This comes from our deep experience with performance optimizations in each part of this stack.
- We Look across the traditional silos (DB analytics, ML analytics, DL analytics) which opens doors for seeing new innovations and performance opportunities.
- What most people see as analytics pipeline we see as an analytics cycle. Again this opens up new opportunities.
Remember this kind of thinking comes about from having experience with full stack innovations/experience with performance optimization on every type of enterprise application. http://www.aiperf.com/bio/unique-experiences.html.
Our recent DL innovation paper:
“Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)”
Brad Carlile, Guy Delamarter, Paul Kinney, Akiko Marti & Brian Whitney
https://arxiv.org/pdf/1710.09967.pdf
…We are of course working on other AI innovations… we’ll keep you posted.