AI products built on serverless platforms could be aided by Surrey techniques | University of Surrey

The development of machine learning (AI) software built on serverless platforms -- thought to be critical for future smart railway networks and autonomous vehicle travel -- could be boosted by unprecedented new methods, finds a study by the University of Surrey. The work is used to serve Real Time predictions on train delays through Emu Analytics’ Flo.w platform.
In a paper presented at IEEE SOCA [https://ieeexplore.ieee.org/abstract/document/8953024], a team of computer scientists from the University of Surrey detail four innovative techniques that allow AI software to work on restrictive, serverless platforms without compromising the performance of the software.
The team also present a novel deep reinforcement learning (DRL) approach to the stochastic optimisation problem of controlling Gene Regulatory Networks (GRNs); with implications for targeted therapeutics. Paper presented at Complex Networks [https://link.springer.com/chapter/10.1007%2F978-3-030-36687-2_60].
Sotiris Moschoyiannis: https://www.linkedin.com/in/sotmos/
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