This content is being retired and may not be updated in the future. The support for Machine Learning Server will end on July 1, 2022. For more information, see What's happening to Machine Learning Server?
Machine Learning Server provides powerful R and Python function libraries for data science and machine learning on small-to-massive data sets, in parallel on local or distributed systems, with modern algorithms for predictive analytics, supervised learning, and data mining.
Functionality is delivered through proprietary R and Python packages, internal computational engines built on open-source R and Python, tools, solutions, and samples.
In this article, learn about the capabilities introduced in the latest packages and tools. If you develop in R, you might also want to review feature announcements from recent past releases.
Refactored tooling for Machine Learning Server configuration. The new command-line interface is similar to Azure CLIs and offers full parity with the previous utility.
Use the tool to enable web service deployment, web and compute node designations, and remote execution (R only). You can also manage ports, nodes, credentials; run diagnostic reports; and test the capacity and throughput of web services you create.
You can construct a dedicated session pool for a specific web service to provide ready-to-use connections with preloaded dependencies for fast access to production code. This capability is in addition to the generic session pools that you can establish server-wide as a shared resource for all web services. Configure in R | Configure in Python.
The 9.2.1 release was the first release of Machine Learning Server - based on R Server - expanded with Python libraries for developers and analysts who code in Python.
The first release of this library, used for distributed computing, local compute context, remote compute context for SQL Server and Spark 2.0-2.1 over the Hadoop Distributed File System (HDFS), and high-performance algorithms for Python. This library is similar to RevoScaleR for R.
Contains Python code, models, and model assets. Accepts specific inputs and provides specific outputs for integration with other services and applications.
The utility simplifies registration of compute nodes with web nodes.
Python development
In Machine Learning Server, Python libraries used in script execute locally, or remotely in either Spark over Hadoop Distributed File System (HDFS) or in a SQL Server compute context. Libraries are built on Anaconda 4.2 over Python 3.5. You can run any 3.5-compatible library on a Python interpreter included in Machine Learning Server.
Note
Remote execution is not available for Python scripts. For information about to do this in R, see Remote execution in R.
R development
R function libraries are built on Microsoft R Open (MRO), Microsoft's distribution of open-source R 3.4.1.
The last several releases of R Server added substantial capability for R developers. To review recent additions to R functionality, see feature announcements for previous versions.
Manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning and MLflow.