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.

翻译：终端用户可以从无服务器平台获取函数即服务，这些平台承诺以更低的托管成本、高可用性、容错能力和动态灵活性来托管称为微服务的独立函数。机器学习工具被证明具有可靠的实用性，而利用这些工具创建的服务正面临大规模增长的需求。无服务器平台特别适合托管这些用于大规模应用的机器学习服务。这些平台以其成本效益、容错性、资源扩展能力、强大的通信API和全球覆盖范围而闻名。然而，机器学习服务与网络服务存在差异，而这些无服务器平台最初是为托管网络服务而设计的。本研究旨在探讨这些无服务器平台如何处理机器学习工作负载。我们考察了在无服务器平台之一——Google Cloud Run上的机器学习性能，该平台是一种未配备GPU且非为机器学习应用部署设计的基础设施。