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What's New in Microsoft R Server 9. This release of R Server, built on open source R 3. R Server through machine learning capabilities, operationalization enhancements with real- time scoring and dynamic scaling of VMs, and integration with sparklyr. Here are highlights of what you can do with this release: Use pre- trained deep neural network models for sentiment analysis and image featurization. For instructions on how to install these models, see How to install and deploy pre- trained machine learning models with Microsoft.

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ML. For quickstarts that show how to use pretrained models for sentiment analysis and image featurization, see Samples for Microsoft. ML. Run Microsoft. ML transforms and algorithms with Apache Spark on a HDInsight cluster for scalable and extremely high performance data management, analysis, and visualization. For installation instructions, see Install R Server 9. Cloudera distribution of Apache Hadoop (CDH). For a tutorial walking you through the process, see Practice data import and exploration on Apache Spark. Deploy Ensemble methods that use a combination of learning algorithms to provide better predictive performance than the algorithms could individually.

The approach is used primarily in the Hadoop/Spark environment for training across a multi- node cluster. But it can also be used in a single- node/local context. Perform real- time scoring in SQL Server to execute R scripts from T- SQL without having to call an R interpreter. Scoring a model in this way reduces the overhead of multiple process interactions and provides much faster prediction performance in enterprise production scenarios. Create text classification models for problems such as sentiment analysis and support ticket classification. Train deep neural nets with GPU acceleration in order to solve complex problems such as retail image classification and handwriting analysis. Work with high- dimensional categorical data for scenarios like online advertising click- through prediction.

Solve many other common machine learning tasks such as churn prediction, loan risk analysis, and demand forecasting using state- or- the- art, fast and accurate algorithms. Train models 2x faster than logistic regression with the Fast Linear Algorithm (SDCA). Train multilayer custom nets on GPUs up to 8x faster with GPU acceleration for Neural Nets. Reduce training time up to 1. Interoperability with sparklyr. Within the same R script, you can mix and match functions from Revo. Scale. R and Microsoft ML packages with popular open source packages like sparklyr and through it, H2.

O. To learn more, see Use R Server with sparklyr (step- by- step examples). In this use case, modeling (or processing) occurs over data collected for singular entities (such as devices, people, products, days) where the per- entity data sets are relatively small in comparison with big data scenarios so often typical of R workloads. In this release, you can leverage the new rx. Exec. By function against unordered data, have it sorted and grouped into partitions (one partition per entity), and then processed in parallel using whatever function or operation you want to run. For example, to project the health outcomes of individuals in a fitness study, you could run a prediction model over data collected about each person. Supported compute context includes Rx. Spark and Rx. In.

SQLServer. Users are assigned to roles using the security groups defined in your organization's Active Directory /LDAP or Azure Active Directory server. Simply use a supported model object and set the service. Type = Realtime argument at publish time. Expanded platform support in future releases. Learn more about Realtime web services. Asynchronously batch processing for large input data: Web services can now be consumed asynchronously via batch execution. The Asynchronous Batch approach involves the execution of code without manual intervention using multiple asynchronous API calls on a specific web service sent as a single request to R Server.

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Previously, web services could only be consumed using the Request- Response method. Learn more about asynchronous batch consumption. Autoscaling of a grid of web and compute nodes on Azure. A script template will be offered to easily spin up a set of R Server VMs in Azure, configure them as a grid for operationalizing analytics and remote execution.

This grid can be scaled up or down based on CPU usage. Read about the differences between Deploy. R and R Server 9. Operationalization. Executing remotely. Asynchronous remote execution is now supported using the mrsdeploy R package. This is particularly useful when you are running scripts that have long execution times.

Learn more about asynchronous remote execution. R Server deployment and administration in Cloudera Manager. R Server for Hadoop installation is improved for Cloudera distribution including Apache Hadoop (CDH) on Red. Hat Linux (RHEL) 7. On this installation configuration, you can easily deploy, activate, deactivate, or rollback a distribution of R Server using Cloudera Manager. For details, see Install R Server on CDH.

SQL Server R Services and Machine Learning Services. In SQL Server 2. 01.

Microsoft introduced SQL Server R Services, a feature that supports enterprise- scale data science by integrating the R language with SQL Server database engine. In SQL Server 2. 01. Python language. To reflect the support for multiple languages, as of CTP 2. SQL Server R Services has also been renamed as Machine Learning Services (In- Database). To read up on the latest changes in CTP 2.

SQL Server 2. 01. What's new for R in SQL Server in the SQL Server product documentation. New and updated packages. The following packages have been updated in Microsoft R Server and Microsoft R Client: The Revo. Scale. R package has been updated to version 9. The mrsdeploy package has been updated to version 1.

The curl package has been updated to version 2. The jsonlite package has been updated to version 1. Revo. Scale. R 9. Function Updates. Function. Status.

Changesrx. Exec. By. New. Enables parallel processing of partitioned data in Spark and SQL Server compute contexts. Exec. By. Partition.

New. Run analytics computation in parallel on individual data partitions split from an input data source based on the specified variables. Data. Step. Enhanced. Multithreaded support. Get. Partitions. New. Gets the partitions of a previously partitioned Xdf data source. Get. Sparklyr. Connection. New. Get a Spark compute context with sparklyr interop.

Import. Enhanced. Multithreaded support. Merge. Enhanced. Merging data frames in Spark compute context. Rx. Orc. Data. New. Create data sets based on data stored in Optimized Row Columnar (ORC) format. Serialize. Model. New. Serializes a Revo.

Scale. R model so that it can be saved to disk or loaded into a SQL Server database table. Serialized models are requred for real- time scoring. Alamat Web Untuk Download Lagu Dj Mp3. Spark. Cache. Data. New. Set the Cache flag in a Spark compute context. Sync. Packages. New. Copies packages from a user table in a SQL Server database to a location on the file system so that R scripts can call functions in those packages. Function Updates.

Previous releases. If you haven't upgraded recently, you can review the feature announcments from the last several releases of Microsoft R Server to learn about cumulative updates. Announcements. This release of R Server, built on open source R 3. Key features in this release include the following: Related Documents.

New and updated packages in 9. Microsoft Machine Learning algorithms (Microsoft. ML package) is a collection of functions for incorporating machine learning into R code or script that executes on R Server and R Client. It's available in the following Microsoft R products: R Server for Windows, R Client for Windows, and SQL Server R Services. Availability for Linux, Hadoop, and Azure HDInsight is projected for the first quarter of 2. To learn more, see Overview of Microsoft.