Performance and Cost Comparison of Cloud Services for Deep Learning Workload

Abstract

Many organizations are migrating their on-premise artificial intelligence workloads to the cloud due to the availability of cost-effective and highly scalable infrastructure, software and platform services. To ease the process of migration, many cloud vendors provide services, frameworks and tools that can be used for deployment of applications on cloud infrastructure. Finding the most appropriate service and infrastructure for a given application that results in a desired performance at minimal cost, is a challenge. In this work, we present a methodology to migrate a deep learning model based recommender system to ML platform and serverless architecture. Furthermore, we show our experimental evaluation of the AWS ML platform called SageMaker and the serverless platform service known as Lambda. In our study, we also discuss performance and cost trade-off while using cloud infrastructure.