  End-users can get functions-as-a-service from serverless platforms, which
promise lower hosting costs, high availability, fault tolerance, and dynamic
flexibility for hosting individual functions known as microservices. Machine
learning tools are seen to be reliably useful, and the services created using
these tools are in increasing demand on a large scale. The serverless platforms
are uniquely suited for hosting these machine learning services to be used for
large-scale applications. These platforms are well known for their cost
efficiency, fault tolerance, resource scaling, robust APIs for communication,
and global reach. However, machine learning services are different from the
web-services in that these serverless platforms were originally designed to
host web services. We aimed to understand how these serverless platforms handle
machine learning workloads with our study. We examine machine learning
performance on one of the serverless platforms - Google Cloud Run, which is a
GPU-less infrastructure that is not designed for machine learning application
deployment.
