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Serverless computing has emerged as a compelling paradigm for the developmentand deployment of a wide range of event based cloud applications. At the sametime, cloud providers and enterprise companies are heavily adopting machinelearning and Artificial Intelligence to either differentiate themselves, orprovide their customers with value added services. In this work we evaluate thesuitability of a serverless computing environment for the inferencing of largeneural network models. Our experimental evaluations are executed on the AWSLambda environment using the MxNet deep learning framework. Our experimentalresults show that while the inferencing latency can be within an acceptablerange, longer delays due to cold starts can skew the latency distribution andhence risk violating more stringent SLAs.

Serving deep learning models in a serverless platform