The document discusses the application of machine learning for database developers, focusing on data integration, exploratory data analysis, and operationalization using tools like TensorFlow and Azure. It emphasizes Microsoft's goal to democratize AI and provides various scripts and methodologies for implementing machine learning processes. Additionally, it includes links for further learning resources and expresses gratitude for feedback.

1. Machine Learning
für Datenbankentwickler
Sascha Dittmann
Cloud Solution Architect, Data & AI
Microsoft Deutschland GmbH

2. Quelle: Stefan Bayer / pixelio.de

3. rxLinMod(…)

4. “
OUR GOAL IS TO DEMOCRATIZE A.I.
TO EMPOWER EVERY PERSON
AND EVERY ORGANIZATION
TO ACHIEVE MORE
”SATYA NADELLA

7. Ingest Transform Explore Model DeployÚ Ú Ú Ú Ú
Score Visualize MeasureÚ Ú ÚÚ
Model
Score
ƒ(x)
Preparation Modeling
Operationalization
https://inseaddataanalytics.github.io/INSEADAnalytics/CRISP_DM.pdf

8. Preparation data integration

9. Data Integration

10. Exploratory
Data Analysis
Quelle: Claudia Hautumm / pixelio.de

11. Exploratory
Data Analysis

12. Removing
Dublicates

14. Dealing with
Outliers

15. Data
Transformation
Quelle: Karl-Heinz Laube / pixelio.de

16. Modeling
data integration

17. https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-cheat-sheet

18. y = 0,5 + 1,3x
hΘ(x) = Θ0 + Θ1x
General Function:
hΘ(x) = Θ0 + Θ1x1 + Θ2x2 + Θ3x3 + Θ4x4 + ...

19. Hypothesis: hΘ(x) = Θ0 + Θ1x
Amount of training data: m

21. EXECUTE sp_execute_external_script
@language = N'R'
, @script = N'x <- as.matrix(InputDataSet);
y <- array(dim1:dim2);
OutputDataSet <- as.data.frame(x %*% y);'
, @input_data_1 = N'SELECT [Col1] from MyData;’
, @params = N'@dim1 int, @dim2 int’
, @dim1 = 12, @dim2 = 15
WITH RESULT SETS (([Col1] int, [Col2] int, [Col3] int, [Col4] int));

22. launchpad.exe
sp_execute_external_script
sqlservr.exe
Named pipe
Each SQL instance
has a launchpad
SQLOS
XEvent
MSSQLSERVER Service MSSQLLAUNCHPAD Service
“What” and “How”
to “launch”
“launcher”
Windows
“satellite” process
sqlsatellite.dll
Windows
“satellite” process
Windows
“satellite” process
Windows
“satellite” process
Windows
“satellite” process

24. Pre-Day Event
Einstieg in Machine Learning mit TensorFlow und Azure

25. Operationalization

26. DECLARE @mymodel VARBINARY(MAX) = (
SELECT model
FROM ml_models
WHERE model_name = 'RentalModel'
AND model_version = 'v1')

27. EXECUTE sp_execute_external_script
@language = N'R'
, @script = N'mod <- unserialize(as.raw(model));
Scores <- rxPredict(modelObject = mod, data = InputDataSet,
outData = NULL, predVarNames = "Score",
type = "response", writeModelVars = FALSE,
overwrite = TRUE);
OutputDataSet <- as.data.frame(Scores$Score);'
, @input_data_1 = N'SELECT * FROM dbo.rental_data'
, @params = N’@model varbinary(max)’
, @model = @mymodel
WITH RESULT SETS (([Score] float));

28. SELECT d.*, p.*
FROM PREDICT(MODEL = @mymodel, DATA = dbo.rental_data AS d)
WITH (RentalCount_Pred FLOAT) AS p;
EXEC sp_rxPredict @model = @mymodel
, @inputData = N'SELECT * FROM dbo.rental_data'

30. https://www.coursera.org/learn/machine-learning
https://academy.microsoft.com/en-us/professional-program/tracks/data-science/
https://ptdrv.linkedin.com/z6nu1hl
https://docs.microsoft.com/en-us/sql/advanced-analytics/real-time-scoring
https://docs.microsoft.com/en-us/sql/advanced-analytics/sql-native-scoring

31. Thank you very much for your attention.
Vielen Dank für Eure Aufmerksamkeit.
Ich freue mich über
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