Machine Learning en SQL Server
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Este documento describe cómo SQL Server permite el machine learning mediante la integración de R y Python. SQL Server 2016 y versiones posteriores admiten scripts de R y Python ejecutados mediante la función sp_execute_external_script. Esto permite el desarrollo, entrenamiento y operacionalización de modelos directamente en la base de datos sin mover datos. SQL Server 2017 también incluye soporte para predicciones en tiempo real mediante la función PREDICT.
![SP_execute_external_script
EXEC sp_execute_external_script
@language = N’R’,
@script = N’[Codigo]’,
@input_data_1 = N’[SQL input]’
[ , @input_data_1_name = N‘InputDataSet’ ]
[ , @output_data_1_name = N’OutputDataSet’ ]
[ , @params = N’parameter’ ]
WITH RESULT SETS (([SQL output]));
input_data_1_name and
output_data_1_name are optional
and default to InputDataSet and
OutputDataSet respectively](https://image.slidesharecdn.com/machinelearning-180727223929/85/Machine-Learning-en-SQL-Server-14-320.jpg)
![Operationalized R
EXEC sp_execute_external_script
@language = N’R’,
@script = N’[R code goes here]’,
@input_data_1 = N’[SQL input]’
[ , @input_data_1_name = N‘InputDataSet’ ]
[ , @output_data_1_name = N’OutputDataSet’ ]
[ , @params = N’parameter’ ]
WITH RESULT SETS (([SQL output]));
If output is a model or plot,
specify varbinary(max) in
WITH RESULT SETS](https://image.slidesharecdn.com/machinelearning-180727223929/85/Machine-Learning-en-SQL-Server-15-320.jpg)
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Machine Learning en SQL Server
- 1. Machine Learning con SQL Server www.triggerdb.com http://blog.triggerdb.com/maxi
- 2. BIOGRAPHY POINT THREE Especialista en Data Platform con mas de 15 años de experiencia en trabajos de (tuning – seguridad – migraciones – Desarrollo – BI – Alta disponibilidad – Big data y Machine Learning Maximiliano Accotto BIOGRAPHY POINT ONE Owner TriggerDB Consulting SRL | www.triggerdb.com BIOGRAPHY POINT TWO Microsoft MVP Data Platform desde el año 2005, miembro de SQLPass Argentina y speaker para Microsoft en diferentes eventos desde el año 2005 https://twitter.com/maxiaccotto https://www.linkedin.com/in/maxiaccotto/
- 3. Acerca de TriggerDB Consulting Nacimos en Argentina en el año 2005, desde entonces estamos ayudando a distintas organizaciones en el mundo en el manejo y entrenamiento de las plataformas de datos de Microsoft (SQL Server, PowerBI, Azure, Big Data,etc) con la meta de traspasarles a nuestros clientes los conocimientos adquiridos día a día. Somos Microsoft Partner certificados en Data platform / Data Analytics y PowerBI Líneas de Contacto www.triggerdb.com [email protected] https://www.facebook.com/triggerdb/
- 4. ¿Que es machine Learning? Es una rama de la inteligencia artificial cuyo objetivo es desarrollar técnicas que permitan a las computadoras aprender.
- 5. Regular Database + App Intelligence Database + App Application + Intelligence Database Application Intelligence + Database VS
- 6. ¿Porque ML en SQL Server? Eliminar el movimiento de datos Operación ML scripts y modelos Performance y escalabilidad Enterprise SQL Transformations Relational data Analytics library
- 7. Ahora Attribute Selection sp_execute_external_script ‘R code’ Attribute Selection Tool C++/R Segmentation Tool C++/R Segmentation sp_execute_external_script ‘R code’ Scoring sp_execute_external_script ‘R code’ Data tables SQL Server SQL Server Stored Proc Data tables Antes
- 8. SQL Server Machine Learning Services • SQL Server 2016 • R support (3.2.2 version) • Microsoft R Server • SQL Server 2017 • Scoring native en TSQL usando PREDICT function (+Linux support) • EXTERNAL LIBRARY DDL para el manejo de paquetes R • Ejecucion en batch para la entrada de datos • Soporte para R (3.3.3 version) • Soporte para Python (Anaconda 3.5.2)
- 9. Machine Learning Server • Soporte Multi-plataforma • Windows, Linux, Hadoop, SQL Server • Microsoft R Server • RevoScaleR, MicrosoftML, olapR, sqlrutils packages • Uso de Web services para operar. • Microsoft Machine Learning Server • Soporte de R & Python • revoscalepy, microsoftml python libraries • rxExecBy
- 10. Comparación ScaleR Performance US flight data for 20 years Linear Regression on Arrival Delay Run on 4 core laptop, 16GB RAM & 500GB SSD
- 11. Any R/Python IDE Data Scientist Workstation Script Results Execution 1 3 Data Scientists - Exploración de Datos y Desarrollo de Modelos SQL Server 2 R/Python Runtime Machine Learning Services train <- RxSqlServerData(query, connectionString, computeContext) rxLogit(formula, train)
- 12. Application exec sp_execute_external_script @language = ‘Python’ , @script = -- Python code -- The stored procedure contains R or Python code and executes in-database Application Developer - Operacionalización de modelos Stored Proc call Results 1 3 Execution SQL Server 2 R/Python Runtime Machine Learning Services
- 13. Trabajo del DBA: Habilitar ML en SQL Server Enable External scripts – Exec sp_configure ‘external scripts enabled’, 1 – RCONFIGURE SQL Server 2016 O superior
- 14. SP_execute_external_script EXEC sp_execute_external_script @language = N’R’, @script = N’[Codigo]’, @input_data_1 = N’[SQL input]’ [ , @input_data_1_name = N‘InputDataSet’ ] [ , @output_data_1_name = N’OutputDataSet’ ] [ , @params = N’parameter’ ] WITH RESULT SETS (([SQL output])); input_data_1_name and output_data_1_name are optional and default to InputDataSet and OutputDataSet respectively
- 15. Operationalized R EXEC sp_execute_external_script @language = N’R’, @script = N’[R code goes here]’, @input_data_1 = N’[SQL input]’ [ , @input_data_1_name = N‘InputDataSet’ ] [ , @output_data_1_name = N’OutputDataSet’ ] [ , @params = N’parameter’ ] WITH RESULT SETS (([SQL output])); If output is a model or plot, specify varbinary(max) in WITH RESULT SETS
- 16. Tipos de salida 1. Dataset • Standard resultset of rows and columns • Data types will vary 2. Plot • Static images • Binary 3. Model • Trained models such as linear regression, naïve bayes, etc. • Binary
- 17. Output a Dataset
- 18. Output a Plot
- 19. Output a Model
- 20. Realtime Predictions usando Scoring nativo • PREDICT function • No depende de R o Python runtime • Habilitado on SQL Server tanto en Windows como Linux • Uso • Single or small number of rows scoring • Highly concurrent scoring scenarios • Predict during INSERT, UPDATE, MERGE statements • Requirements • Models built using RevoScaleR or revoscalepy • rxLinMod, rxLogit, rxBTrees, rxDTree, rxDForest • Serialized using rxSerializeModel (R) or rx_serialize_model (Python)
- 21. PREDICT syntax
- 22. 1,000,000 predictions per second https://blogs.technet.microsoft.com/dataplatforminsider/2016/10/11/1000000-predictions-per- second/
- 24. Próximos eventos Curso online SQL Server performance Tuning (agosto 2018) http://www.triggerdb.com/event/curso-a-distancia-performance-y-tuning-sql-server/ Curso online PowerBI (septiembre 2018) http://www.triggerdb.com/event/curso-powerbi-a-distancia/
Notas del editor
- #6: 5
- #11: Microsoft R Server has no data size limits in relation to size of available RAM. When open source R operates on data sets that exceed RAM it will fail. In contrast Microsoft R Server scales linearly well beyond RAM limits and parallel algorithms are much faster. The figure on the left shows the results of a benchmark test comparing the performance of OSR and a ScaleR algorithm. as can be seen on the fgure, When OSR operates on data sets that exceed RAM (about 300K observations in a dataframe), it will fail. On the other hand it can be seen from the plot that ScaleR has no data size limit in relation to the size of the RAM. The ScaleR algorithm is seen to scales linearly well beyond the limits of the RAM (over 5M observations in a dataframe) and the parallel algorithms are much faster. The table on the right highlights the massive speed boost Microsoft R Server provides when running a linear regression algorithm on 20 years of US flight data. You will also notice that data sets exceeding the 16GB of available RAM failed with memory errors when running the calculations using Open R.
- #12: Microsoft Tech Summit FY17
- #13: Microsoft Data Amp