菩萨-在容器上快速部署AI

S. Rao, Pradyumna S, Subramaniam Kalambur, D. Sitaram
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引用次数: 2

摘要

基于云的机器学习在所有垂直行业中变得越来越重要,因为所有组织都希望利用ML和AI来解决新兴市场的现实问题。但是,将这些服务合并到业务解决方案中是一项艰巨的任务,主要是因为从开发到部署需要付出巨大的努力。我们提出了一种新颖的想法,使用户能够通过可扩展的机器学习即服务(MLaaS)产品轻松指定、创建、训练和快速部署机器学习模型。MLaaS是在基于容器的PaaS环境中为云上的web应用程序提供的端到端微服务套件。我们的实现为租户提供了一个直观的基于web的GUI,通过几个快速的步骤就可以使用这些服务。通过为各种用例训练ML模型,并在部署时间、资源使用和训练指标等因素上对它们进行比较,可以证明我们服务的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bodhisattva - Rapid Deployment of AI on Containers
Cloud-based machine learning is becoming increasingly important in all verticals of the industry as all organizations want to leverage ML and AI to solve real-world problems of emerging markets. But, incorporating these services into business solutions is a goliath task, mainly due to the sheer effort necessary to go from development to deployment. We present a novel idea that enables users to easily specify, create, train and rapidly deploy machine learning models through a scalable Machine-Learning-as-a-Service (MLaaS) offering. The MLaaS is provided as an end-to-end microservice suite in a container-based PaaS environment for web applications on the cloud. Our implementation provides an intuitive web-based GUI for tenants to consume these services in a few quick steps. The utility of our service is demonstrated by training ML models for various use cases and comparing them on factors like time-to-deploy, resource usage and training metrics.
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