Machine learning services with SQL Server 2017
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SQL Server 2017 introduces Machine Learning Services with two independent technologies: R and Python. The purpose of this presentation is 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.
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Machine learning services with SQL Server 2017
- 1. Machine Learning Services with SQL Server 2017MARK TABLADILLO PH.D. LEAD DATA SCIENTIST, MICROSOFT JULY 31, 2017
- 5. Microsoft and Open Source SQL Server 2017 on Linux Nearly 1/3 of Virtual Machines (IAAS) on Azure are Linux https://news.microsoft.com/bythenumbers/azure-virtual Purchase of RevolutionR R Distribution Microsoft R Client R inside Azure Machine Learning, Power BI, SQL Server, Jupyter Python inside Azure Machine Learning, SQL Server, Jupyter Cloud Shell In Azure (preview): yes, we mean Bash https://azure.microsoft.com/en-us/features/cloud-shell/ Microsoft now the leading contributor on GitHub
- 6. Focus 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.
- 7. SQL Server 2017 MAJOR FEATURES
- 8. Gartner Review October 2016
- 11. SQL Server on Linux Possible with Drawbridge Over 1M Docker Downloads Whitepaper on Linux https://info.microsoft.com/SQL Server-on-Linux-Open-source- enterprise-environment.html Video – Overview of SQL Server on Linux https://channel9.msdn.com/e vents/connect/2016/101
- 12. Microsoft Release Acronyms CTP RC Community Technology Preview Release Candidate
- 13. Versions of Microsoft SQL Server https://docs.microsoft.com/en-us/sql/sql-server/editions-and- components-of-sql-server-2017 Enterprise Many data scientists will use the free developer version (not intended for production) Since we are still at RC (Release Candidate): Free 180 day evaluation version (Enterprise equivalent) Windows Docker image Linux Docker image https://www.microsoft.com/en-us/evalcenter/evaluate-sql-server-2017- ctp
- 14. Data Science & AI
- 16. Team Data Science Process https://github.com/Azure/Microsoft-TDSP
- 17. • A statistics programming language • Data analysis & visualization capabilities • Majority of data scientists use R • Thriving user groups worldwide • Vibrant open Source community • 10,000 + free algorithms in CRAN • New and recent grad’s use it #1 Language Advanced Analytics 2.5M+ Users Open Biggest Ecosystem • Strong ties to academia feeds ever- growing machine learning capabilities What is • Constantly innovating
- 18. but, Open Source R is not Enterprise Class 76% of analytic professionals report using R 36% select R as their primary tool R Usage Growth Rexer Data Miner Survey 2007-2015 Inadequate Modeling Performance ? ? Lack of Commercial Support Complex Deployment Processes Limited Data Scale
- 19. Our data science tool that allows you to do high performance analytics on production data, running locally on your computer.
- 24. Classified as Microsoft Confidential • Turn R analytics Web services in one line of code; • Swagger-based REST APIs, easy to consume, with any programming languages, including R! • Deploying web service server to any platform: Windows, SQL, Linux/Hadoop • On-prem or in cloud • Fast scoring, real time and batch • Scaling to a grid for powerful computing with load balancing • Diagnostic and capacity evaluation tools • Enterprise authentication: AD/LDAP or AAD • Secure connection: HTTPS with SSL/TLS 1.2 • Enterprise grade high availability
- 25. Classified as Microsoft Confidential • Turn R analytics Web Service in one line of code; • Swagger-based REST APIs, easy to consume, with any programming languages, including R! • Deploying Web Service server to any platform: Windows / SQL / Linux/Hadoop • On Prem or in Cloud • Fast scoring, real time and batch • Scaling to a grid for powerful computing with load balancing • Diagnostic and capacity evaluation tools • Enterprise authentication: LDAP / AD/ AAD • Secure connection: HTTPS with SSL.TSL1.2 • Enterprise grade High Availability
- 26. Classified as Microsoft Confidential Data Scientist Developer Easy Integration Easy Deployment Easy Setup ▪ In-cloud or on-prem ▪ Adding nodes to scale ▪ High availability & load balancing ▪ Remote execution server Microsoft R Server configured for operationalizing R analytics Microsoft R Client (mrsdeploy package) Data Scientist Easy Consumption publishServiceMicrosoft R Client (mrsdeploy package)
- 27. Classified as Microsoft Confidential Build the model first Deploy as a web service instantly
- 28. Classified as Microsoft Confidential Function Description publishService Publish a predictive function as a Web Service deleteService Delete a Web Service getService Get a Web Service ListServices List the different published web services serviceOption Retrieve, set, and list the different service options updateService Updates a Web Service
- 29. Classified as Microsoft Confidential Data Scientist # Run the following code in R swagger <- api$swagger() cat(swagger, file = "swagger.json", append = FALSE) Generate Swagger Docs for Web Services Developer Popular Swagger Tools: AutoRest or Code Generator AutoRest.exe -CodeGenerator CSharp -Modeler Swagger - Input swagger.json - Namespace Mynamespace Run Swagger tools to generate code Developer Write a few code to consume the service
- 30. Classified as Microsoft Confidential Share / Reuse R code / functions • Not just models, a data scientist can share any functional code as a service. • Other data scientists can explore in the repository to re-use those functions. Enable Model Management capabilities • A Predictive Web Service = “Model” + “Prediction Script” • R Server hosts all those services Central Repo of Models • Each service has a version tag Model Version Control • All versions are active Model Roll Back (to any version) • A service can be accessed by any authorized users • Model reuse • Model validation and monitoring by QA team After service is published, I can test if the service works as expected right away
- 31. Classified as Microsoft Confidential ▪ Built-in remote execute functions in R Client/R Server ▪ Generate Diff report to reconcile local and remote ▪ Execute .R script or interactive R commands ▪ Results come back to local ▪ Generate working snapshots for resume and reuse ▪ IDE agnostic R Client (mrsdeploy package) R Server configured to Remote Execute R Scripts (Support Window Server, Linux Server, Hadoop ) ▪ Execute R Scripts ▪ Snapshot remote env. ▪ Logout remote server ▪ Login remote server ▪ Generate Diff report ▪ Reconcile Environment
- 32. Classified as Microsoft Confidential Snapshot Functions createSnapshot Create a snapshot of the remote session (workspace and working directory) loadSnapshot Load a snapshot from the server into the remote session (workspace and working directory) listSnapshots Get a list of snapshots for the current user downloadSnapshot Download a snapshot from the server deleteSnapshot Delete a snapshot from the server Remote Objects Management listRemoteFiles Get a list of files in the working directory of the remote session deleteRemoteFile Delete a file from the working directory of the remote R session getRemoteFile Copy a file from the working directory of the remote R session putLocalFile Copy a file from the local machine to the working directory of the remote R session getRemoteObject Get an object from the remote R session putLocalObject Put an object from the local R session and load it into the remote R session getRemoteWorkspace Take all objects from the remote R session and load them into the local R session putLocalWorkspace Take all objects from the local R session and load them into the remote R session Remote Connection remoteLogin Remote login to the R Server with AD or admin credentials remoteLoginAAD Remote login to R Server server using Azure AD remoteLogout Logout of the remote session on the DeployR Server. Remote Execution remoteExecute Remote execution of either R code or an R script remoteScript Wrapper function for remote script execution diffLocalRemote Generate a 'diff' report between local and remote pause Pause remote connection and back to local resume Return the user to the 'REMOTE >' command prompt
- 33. Classified as Microsoft Confidential • Turn R analytics Web Service in one line of code; • Swagger-based REST APIs, easy to consume, with any programming languages, including R! • Deploying Web Service server to any platform: Windows / SQL / Linux/Hadoop • On Prem or in Cloud • Fast scoring, real time and batch • Scaling to a grid for powerful computing with load balancing • Diagnostic and capacity evaluation tools • Enterprise authentication: LDAP / AD/ AAD • Secure connection: HTTPS with SSL.TSL1.2 • Enterprise grade High Availability
- 34. Classified as Microsoft Confidential ModelPrepare SQL 2016 OperationalizeOperationalize R & ScaleR Models CRAN R Models AzureML Web Services R Server VMs ModelPrepare Operationalize T-SQL/Stored Procedure Operationalize R Server On PremCloud Deploy to SQL Server 2016 Deploy to Hadoop / Linux Server / Windows Server
- 35. Classified as Microsoft Confidential • • • • • • • ModelPrepare OperationalizeOperationalize R & ScaleR Models R Models On Prem
- 36. Classified as Microsoft Confidential • • • • • • • • ModelPrepare Operationalize SQL, HDFS R & ScaleR Models On Prem • R Server • T-SQL/Stored Procedure
- 37. Classified as Microsoft Confidential Product Platforms Modeling Operationalization R Server for Windows Windows Server 2012 - 2016 Same as modeling R Server for Linux Red Hat Enterprise Linux 6.X and 7.X 7.x R Server for Linux SUSE Enterprise SLES 11 will support in future release R Server for Linux Ubuntu 14.04 LTS, 16.04 LTS Same as modeling R Server for Linux CentOS 6.X and 7.X 7.x R Server for Hadoop Red Hat and SUSE Enterprise RHEL 6.x and 7.x, SUSE SLES11 RHEL 7.x • • •
- 38. Classified as Microsoft Confidential • Turn R analytics Web Service in one line of code; • Swagger-based REST APIs, easy to consume, with any programming languages, including R! • Deploying Web Service server to any platform: Windows / SQL / Linux/Hadoop • On Prem or in Cloud • Fast scoring, real time and batch • Scaling to a grid for powerful computing with load balancing • Diagnostic and capacity evaluation tools • Enterprise authentication: LDAP / AD/ AAD • Secure connection: HTTPS with SSL.TSL1.2 • Enterprise grade High Availability
- 39. Classified as Microsoft Confidential • Easily scale up a single server to a grid to handle more concurrent requests • Load balancing cross compute nodes • A shared pool of warmed up R shells to improve scoring performance. R Client
- 40. Classified as Microsoft Confidential • Health check node configuration • Get system status • Trace R code execution • Trace service execution • Evaluate grid capacity • Simulate traffic per service • Configure with # of concurrent threads or latency thresholds
- 41. Classified as Microsoft Confidential • Turn R analytics Web Service in one line of code; • Swagger-based REST APIs, easy to consume, with any programming languages, including R! • Deploying Web Service server to any platform: Windows / SQL / Linux/Hadoop • On Prem or in Cloud • Fast scoring, real time and batch • Scaling to a grid for powerful computing with load balancing • Diagnostic and capacity evaluation tools • Enterprise authentication: LDAP / AD/ AAD • Secure connection: HTTPS with SSL.TSL1.2 • Enterprise grade High Availability
- 42. Classified as Microsoft Confidential • Seamless integration with authentication solution: LDAP/AD/AAD • Secure connection: HTTPS encrypted by TLS 1.2/SSL • Compliance with Microsoft Security Development Lifecycle R Client
- 43. Classified as Microsoft Confidential Load Balancer • Server level HA: Introduce multiple Web Nodes for Active-Active backup / recovery, via load balancer • Data Store HA: leverage Enterprise grade DB, SQL Server and Postgres’ HA capabilities
- 44. Connect LinkedIn SlideShare Twitter @marktabnet
- 45. Abstract SQL Server 2017 introduces Machine Learning Services with two independent technologies: R and Python. The purpose of this presentation is 1) to describe major features of this technology for technology managers; 2) to outline use cases for architects; and 3) to provide demos for developers and data scientists.