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Deploying Machine Learning Models as Microservices Using Docker


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Deploying Machine Learning Models as Microservices Using Docker

Deploying Machine Learning Models as Microservices Using Docker
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 24M | 825 MB
Genre: eLearning | Language: English

Modern applications running in the cloud often rely on REST-based microservices architectures by using Docker containers. Docker enables your applications to communicate between one another and to compose and scale various components. Data scientists use these techniques to efficiently scale their machine learning models to production applications. This video teaches you how to deploy machine learning models behind a REST API—to serve low latency requests from applications—without using a Spark cluster. In the process, you'll learn how to export models trained in SparkML; how to work with Docker, a convenient way to build, deploy, and ship application code for microservices; and how a model scoring service should support single on-demand predictions and bulk predictions. Learners should have basic familiarity with the following: Scala or Python; Hadoop, Spark, or Pandas; SBT or Maven; cloud platforms like Amazon Web Services; Bash, Docker, and REST.

Understand how to deploy machine learning models behind a REST API
Learn to utilize Docker containers for REST-based microservices architectures
Explore methods for exporting models trained in SparkML using a library like Combust MLeap
See how Docker builds, deploys, and ships application code for microservices
Discover how to deploy a model using exported PMML with a REST API in a Docker container
Learn to use the AWS elastic container service to deploy a model hosting server in Docker
Pick up techniques that enable a model hosting server to read a model

Deploying Machine Learning Models as Microservices Using Docker

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  1. 使用Docker部署机器学习模型作为微服务 运行在云端的现代应用常依赖于使用Docker容器的基于REST的微服务架构。Docker可以让你的应用相互之间通信,并组成各种组件。数据科学家使用这些技术有效地规划他们的机器学习模型成为产品级应用。本视频将会为你带来如何在REST API之后部署机器学习模型,从程序服务低延迟请求,而无需使用Spark集群。在这一过程中,你将学习如何输出在sparkml中训练的模型;如何处理Docker,一种方便的开发、部署和从微服务中发送应用代码的方法;以及一个记分服务的模型如何支持单一按需预测和海量预测。学习者应当具备对下面知识基本的了解:Scala或Python;Hadoop、spark或Pandas;sbt或maven;如AWS这样的云平台;bash、docker和rest。 主要内容: • 了解如何在REST API背后部署机器学习模型 • 学习利用Docker容器准备基于REST的微服务架构 • 了解输出在sparkml中使用如Combust MLeap这样的额类库训练的模型的方法 • 学习Docker如何开发、部署和向微服务发送应用代码 • 了解如何使用Docker容器中REST API输出的pmml部署模型 • 学习使用AWS灵活的容器服务部署模型容器主机服务器 了解能够让模型主机服务器读取模型的技术
    wilde(特殊组-翻译)7个月前 (12-16)