Key features of Machine Learning ServerThe following features are included in Machine Learning Server. For feature descriptions in this release, see What's New in Machine Learning Server.Expand table Feature categoryDescription R_SERVER R packages for solutions written in R, with an open-source ...
适用于: SQL Server 2016 (13.x) 及更高版本“MicrosoftML”是 Microsoft 的一个 R 包,可提供高性能的机器学习算法。 它包括用于定型和转换、评分、文本和图像分析的功能,以及用于从现有数据中派生值的特征提取功能。 该包包含在 SQL Server 机器学习服务和SQL Server 2016 R Services 中,为大数据提供高性能支...
Microsoft Machine Learning Server BlogWe are moving!This blog is in the process of being migrated to one of the new consolidated SQL Server and Azure...Date: 03/04/2019Build Intelligent Web App with Machine Learning ServiceIn an earlier article: How to operationalize TensorFlow models in Microso...
Machine Learning Server runson-premises and in the cloud, on a variety of operating systems, and can run in a distributed mode if you want to isolate functions on different computers (specifically, as dedicated web and compute nodes).Web servicesare hosted on a server grid on-premises or in...
Machine Learning Server runson-premises and in the cloud, on a variety of operating systems, and can run in a distributed mode if you want to isolate functions on different computers (specifically, as dedicated web and compute nodes).Web servicesare hosted on a server grid on-premises or in...
Applies to:SQL Server 2016 (13.x) and laterAzure SQL Managed Instance This article describes how to get information about installed R packages onMachine Learning Services on SQL Serverand onSQL Server 2019 Big Data Clusters. Example R scripts show you how to list package information such as in...
Learn how to use sqlmlutils to install new R packages to an instance of SQL Server Machine Learning Services.
https://github.com/kingliantop/azurelabs/tree/master/RServerDemo Microsoft R客户端的安装 Microsoft R客户端是一个免费的用于数据科学分析的高性能的工具。他基于开源的R语言构建,所以你可以使用任何开源的R packages,另外R client也支持微软的强大的ScaleR语言,包括使用mrsdeploypackage远程执行。
Find and fix bugs in natural language machine learning models using adaptive testing. - microsoft/adaptive-testing
have a server or cluster of servers with sufficient computational resources to handle the model’s workload. You would also need to have the necessary software and tools installed to run the model, such as a web server, a programming language runtime, and the appropriate machine learning ...