Machine Learning Services (R announcement

• Link
  Get started with R analytics on SQL server
  Jitendra Kulshreshtha Friday, April 20, 2018 11:09 PM

  Overview

  The process of building an end to end solution typically consists of obtaining and cleaning data, data exploration and feature engineering, model training and tuning, and finally deployment of the model in production. Development and testing of the actual code is best performed using a dedicated development environment. For R, that might mean RStudio or R Tools for Visual Studio.

  However, after the solution has been created, you can easily deploy it to SQL Server using Transact-SQL stored procedures in the familiar environment of Management Studio.

  In this tutorial, we assume that you have been given all the R code needed for the solution, and focus on building and deploying the solution using SQL Server.

  • Lesson 1: Download the sample data

    Download the sample dataset and the sample SQL script files to a local computer.

  • Lesson 2: Import data to SQL Server using PowerShell

    Execute a PowerShell script that creates a database and a table on the SQL Server 2017 instance and loads the sample data to the table.

  • Lesson 3: Explore and visualize the data

    Perform basic data exploration and visualization, by calling R packages and functions from Transact-SQL stored procedures.

  • Lesson 4: Create data features using T-SQL

    Create new data features using custom SQL functions.

  • Lesson 5: Train and save an R model using T-SQL

    Build a machine learning model using R in stored procedures. Save the model to a SQL Server table.

  • Lesson 6: Operationalize the model

    After the model has been saved to the database, call the model for prediction from Transact-SQL by using stored procedures.

• Link
  Run machine learning directly on SQL Server !!!
  Jitendra Kulshreshtha Friday, April 20, 2018 11:05 PM

  What is SQL Server Machine Learning Services?

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  • THIS TOPIC APPLIES TO: SQL Server (Windows only)
    

  SQL Server Machine Learning Services is an embedded, predictive analytics and data science engine that can execute R and Python code within a SQL Server database as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL.

  The key value proposition of Machine Learning Services is the power of its proprietary packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network.

  There are two options for using machine learning capabilities in SQL Server:

  • SQL Server Machine Learning Services (In-Database) operates within the database engine instance, where the calculation engine is fully integrated with the database engine. Most installations are this option.
  • SQL Server Machine Learning Server (Standalone) is a non-SQL installation. Although you use SQL Server Setup to install the server, it is completely decoupled from SQL Server. Functionally, it is equivalent to the non-SQL Microsoft Machine Learning Server for Windows.

  R and Python packages

  Support for each language is through proprietary Microsoft packages used for creating and training models of various types, scoring data, and parallel processing using the underlying system resources.

  Because the proprietary packages are built on open-source R and Python distributions, script or code that you run in SQL Server can also call base functions and use third-party packages compatible with the language version provided in SQL Server (Python 3.5 and recent versions of R, currently 3.3.3).

  R	Python	Description
  RevoScaleR	revoscalepy	Functions in these libraries are among the most widely used. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling and analyses are found in these libraries. Additionally, functions in these libraries automatically distribute workloads across available cores for parallel processing, with the ability to work on chunks of data that are coordinated and managed by the calculation engine.
  MicrosoftML	microsoftml	Industry-leading machine learning algorithms for image featurization, classification problems, and more.
  olapR	none	Build or execute an MDX query in R script.
  sqlRUtils	none	Functions for putting R scripts into a T-SQL stored procedure, registering a stored procedure with a database, and running the stored procedure from an R development environment.
  mrsdeploy	none	Primarily used on a non-SQL installation of Machine Learning Server, such as the (Standalone) version. Use this package to deploy and host web services, build scale-out topologies with dedicated web and compute nodes, toggle between local and remote sessions, run diagnostics, and more. For an (In-Database) installation, use this package in a client capacity: for example, to access a web service on a remote server dedicated to running just Machine Learning Services workloads.

  Portability of your custom R and Python code is addressed through package distribution and interpreters that are built into multiple products. The same packages that ship in SQL Server are also available in several other Microsoft products and services, including a non-SQL version called Microsoft Machine Learning Server. Free clients that include our R and Pyton interpreters include Microsoft R Client and the Python libraries.