{"id":15927,"date":"2020-08-28T16:19:44","date_gmt":"2020-08-28T14:19:44","guid":{"rendered":"http:\/\/54.194.80.134.nip.io\/?p=15927"},"modified":"2020-08-31T08:36:10","modified_gmt":"2020-08-31T06:36:10","slug":"integration-von-machine-learning-pipelines-in-die-sap-systemlandschaft","status":"publish","type":"post","link":"https:\/\/www.cubeserv.com\/de\/integration-von-machine-learning-pipelines-in-die-sap-systemlandschaft\/","title":{"rendered":"Integration von Machine Learning Pipelines in die SAP Systemlandschaft"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"15927\" class=\"elementor elementor-15927\" data-elementor-settings=\"{&quot;element_pack_global_tooltip_width&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_width_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;size&quot;:&quot;&quot;,&quot;sizes&quot;:[]},&quot;element_pack_global_tooltip_padding&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_padding_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_tablet&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true},&quot;element_pack_global_tooltip_border_radius_mobile&quot;:{&quot;unit&quot;:&quot;px&quot;,&quot;top&quot;:&quot;&quot;,&quot;right&quot;:&quot;&quot;,&quot;bottom&quot;:&quot;&quot;,&quot;left&quot;:&quot;&quot;,&quot;isLinked&quot;:true}}\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-540cf95 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"540cf95\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d56eb7a\" data-id=\"d56eb7a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-63f41dc elementor-widget elementor-widget-text-editor\" data-id=\"63f41dc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"text-align: justify;\">Wie kann eine Machine Learning Pipeline mit einer graphischen Benutzeroberfl\u00e4che in Ihrer<br \/>SAP Systemlandschaft erstellt und voll-integriert angewendet werden? In diesem Blog-Beitrag zeige ich Ihnen wie es geht.<\/span><\/p><p>Die Screenshots wurden im Rahmen eines Workshops bei der Swiss Data Science Conference 2020 mit SAP Data Intelligence Version 3.0 erstellt.<\/p><p>Als erster Use Case wurde die Preisvorhersage von Autos gew\u00e4hlt. Das entsprechende Datenset ist auf Kaggle zu finden, siehe\u00a0<a style=\"background-color: #ffffff;\" href=\"https:\/\/www.kaggle.com\/bozungu\/ebay-used-car-sales-data\">https:\/\/www.kaggle.com\/bozungu\/ebay-used-car-sales-data<\/a>.\u00a0Der vorherzusagende Wert (output variable) ist der Preis in Euro f\u00fcr den ein Gebrauchtwagen in 2016 angeboten wurde. Die Daten sind in einer SAP HANA Datenbank gespeichert, wodurch ein In-Memory Zugriff und somit eine hohe Performance gew\u00e4hrleistet ist.<\/p><p>Das angewendete Vorgehensmodell basiert auf dem\u00a0Cross Industry Standard Process for Data Mining (CRISP-DM) framework. Die ersten vier Phasen (Gesch\u00e4ftsverst\u00e4ndnis, Datenverst\u00e4ndnis, Modellierung und Evaluation) wurden zun\u00e4chst mit der Unterst\u00fctzung von Jupyter Notebook mit Python umgesetzt. Anschliessend wurde die Parameterkonfiguration des Gradient Boosting Regression Algorithmus mit dem kleinsten\u00a0Root Mean Squared Error (RMSE) verwendet um die Machine Learning Pipeline in der graphischen Benutzeroberfl\u00e4che von SAP Data Intelligence zu modellieren und im letzten Schritt als RESTful API zu deployen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ddc05b4 elementor-widget elementor-widget-heading\" data-id=\"ddc05b4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Data understanding in Python mit Jupyter Notebook<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d0e1ccd elementor-widget elementor-widget-text-editor\" data-id=\"d0e1ccd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In der Phase des &#8222;Datenverst\u00e4ndnis&#8220; bietet die Verwendung von Jupyter Notebooks in der Programmiersprache Python eine Vielzahl von Funktionalit\u00e4ten zur Analyse und der entsprechenden Aufbereitung der Ergebnisse. Im nachfolgenden Screenshot sind verschiedene Werte zu den Spalten in dem verwendeten Datenset zu finden, bspw. die Eindeutigkeit der Werte, Null-Werte sowie Durchschnitts- und Medianwerte. Damit ist bspw. m\u00f6glich herauszufinden ob eine Ausreisser Behandlung notwendig ist und wie das Datenset sinnvoll in Training- und Validierungsdatenset aufgeteilt werden kann.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3a04b3b elementor-widget elementor-widget-image\" data-id=\"3a04b3b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"457\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/08\/JupyterNotebook_Python_DataUnderstandingTooltips-1024x457.png\" class=\"attachment-large size-large wp-image-17705\" alt=\"JupyterNotebook_Python_DataUnderstandingTooltips\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/08\/JupyterNotebook_Python_DataUnderstandingTooltips-1024x457.png 1024w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/08\/JupyterNotebook_Python_DataUnderstandingTooltips-300x134.png 300w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/08\/JupyterNotebook_Python_DataUnderstandingTooltips-768x342.png 768w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/08\/JupyterNotebook_Python_DataUnderstandingTooltips-1536x685.png 1536w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/08\/JupyterNotebook_Python_DataUnderstandingTooltips.png 1911w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89a2c0e elementor-widget elementor-widget-heading\" data-id=\"89a2c0e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Model training and evaluation mit Hybrid Gradient Boosting Regression<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1657fd3 elementor-widget elementor-widget-text-editor\" data-id=\"1657fd3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Nach dem Kennenlernen der Daten ist die Wahl des zu verwendenden Algorithmus auf das Hybrid Gradient Boosting Regression aus SAP Predictive Analytics Library (SAP PAL) gefallen. Die eingetragenen Parameter wurden als Start-Konfiguration gew\u00e4hlt, dann die zu verwendenden Spalten bzw. Features angegeben um im letzten Schritt eine Vielzahl von Trainingsmodellen zu erstellen. F\u00fcr technische und mathematische Detailinformationen zum Gradient Boosting in Python kann ich folgende Webseite empfehlen: <a href=\"https:\/\/towardsdatascience.com\/gradient-boosting-in-python-from-scratch-4a3d9077367\" target=\"_blank\" rel=\"noopener\">Gradient Boosting in Python from Scratch<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3443f95 elementor-widget elementor-widget-image\" data-id=\"3443f95\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"455\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining-1024x455.png\" class=\"attachment-large size-large wp-image-15973\" alt=\"JupyterNotebook_Python_ModelTraining\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining-1024x455.png 1024w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining-300x133.png 300w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining-768x341.png 768w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining-1536x683.png 1536w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining.png 1914w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d32ae29 elementor-widget elementor-widget-text-editor\" data-id=\"d32ae29\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Anschliessend besteht die M\u00f6glichkeit die Qualit\u00e4t der Modelle nach\u00a0verschiedenen statistischen Kennzahlen zu bewerten. Im untenstehenden Screenshot wurde der Root-Mean-Square Error (RMSE) als Kennzahl gew\u00e4hlt. Die Parameter (N_ESTIMATORS und MAX_DEPTH) die zum kleinsten RSME gef\u00fchrt haben, wurden gew\u00e4hlt um das zu verwendende Modell zu trainieren.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7f72d09 elementor-widget elementor-widget-image\" data-id=\"7f72d09\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining2-1024x576.png\" class=\"attachment-large size-large wp-image-15976\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining2-1024x576.png 1024w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining2-300x169.png 300w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining2-768x432.png 768w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining2-1536x864.png 1536w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/JupyterNotebook_Python_ModelTraining2.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-794aace elementor-widget elementor-widget-heading\" data-id=\"794aace\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h2>Verwendung Parameterkonfiguration im SAP Machine Learning Operator zur In-Memory Ausf\u00fchrung<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a3243a elementor-widget elementor-widget-text-editor\" data-id=\"7a3243a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Nach der Modellierung und Evaluation habe ich es in der Vergangenheit h\u00e4ufig erlebt, dass es f\u00fcr Unternehmen eine grosse Herausforderung ist, die entwickelten und evaluierten Modelle in Produktion zu deployen. Die Integration in die bestehenden Gesch\u00e4ftsprozesse sowie die Machine Learning Pipeline \u00fcber verschiedene IT-Systeme (mit unterschiedlichen Programmiersprachen) zu steuern geh\u00f6rten ebenfalls zu den teils untersch\u00e4tzten Aktivit\u00e4ten. Der Data Intelligence Modeler erlaubt diese Integration von IT-Systemen und Programmiersprachen in einer graphischen Benutzeroberfl\u00e4che. Im nachfolgenden Screenshot ist die Data Pipeline ersichtlich, welche zum Training des\u00a0 st\u00e4rksten Modells verwendet wird.\u00a0\u00a0<\/p><p><span style=\"font-size: 1rem;\">Da die Modellierung und Evaluation im CRSIP-DM h\u00e4ufig in mehreren Iterationen durchgef\u00fchrt werden, ist sinnvoll und zugleich hilfreich dies in\u00a0<\/span>Jupyter<span style=\"font-size: 1rem;\">\u00a0Notebook durchzuf\u00fchren. Anschliessend\u00a0 k\u00f6nnen\u00a0<\/span><span style=\"font-size: 1rem;\">der Standard-Komponente &#8222;HANA ML Training&#8220; die Parameter, die zum st\u00e4rksten Modell gef\u00fchrt haben, \u00fcbergeben werden.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e7a7662 elementor-widget elementor-widget-image\" data-id=\"e7a7662\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/cdn.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator.png\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"SAP HANA Machine Learning Operator\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6MTU5MzUsInVybCI6Imh0dHBzOlwvXC93d3cuY3ViZXNlcnYuY29tXC93cC1jb250ZW50XC91cGxvYWRzXC8yMDIwXC8wN1wvU0FQLU1hY2hpbmUtTGVhcm5pbmctT3BlcmF0b3IucG5nIn0%3D\">\n\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator-1024x576.png\" class=\"attachment-large size-large wp-image-15935\" alt=\"SAP HANA Machine Learning Operator\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator-1024x576.png 1024w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator-300x169.png 300w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator-768x432.png 768w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator-1536x864.png 1536w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Machine-Learning-Operator.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ad10575 elementor-widget elementor-widget-text-editor\" data-id=\"ad10575\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In jeder Pipeline im Data Intelligence Modeller k\u00f6nnen verschiedene Operatoren verwendet werden. Ein Beispiel ist das &#8222;HANA ML Training&#8220;. Es kann jedoch bspw. auch der Python Operator verwendet werden, um individuelle Python Code oder R Code zu integrieren (bspw. falls nicht ein Algorithmus aus der SAP PAL verwendet werden soll, wie in diesem Beispiel).\u00a0<\/p><p>Der Zugriff auf eine Vielzahl von Datenbanken, Cloud Anbieter und weitere SAP Produkte wie S\/4HANA ist ebenso m\u00f6glich wie die Steuerung von SAP BW Prozessketten.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bdda1d3 elementor-widget elementor-widget-heading\" data-id=\"bdda1d3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Deployment des Machine Learning Models via RESTful API<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e23ad94 elementor-widget elementor-widget-text-editor\" data-id=\"e23ad94\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Nach der Erstellung des st\u00e4rksten Modells kann dieses nun in einer Consumer-Pipeline via einem RESTful API zur Verf\u00fcgung gestellt werden, wie im nachfolgenden Screenshot abgebildet.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ea26c36 elementor-widget elementor-widget-image\" data-id=\"ea26c36\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"348\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Deployment-Machine-Learning-Model-1024x348.png\" class=\"attachment-large size-large wp-image-15979\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Deployment-Machine-Learning-Model-1024x348.png 1024w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Deployment-Machine-Learning-Model-300x102.png 300w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Deployment-Machine-Learning-Model-768x261.png 768w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Deployment-Machine-Learning-Model-1536x522.png 1536w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Deployment-Machine-Learning-Model.png 1870w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a3c85f9 elementor-widget elementor-widget-heading\" data-id=\"a3c85f9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Aufruf RESTful API zur Preisvorhersage auf Basis des Machine Learning Modells<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16d9165 elementor-widget elementor-widget-text-editor\" data-id=\"16d9165\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Nach erfolgreichen Deployment ist es m\u00f6glich mit einem POST-Request die Schnittstelle zu benutzen, um sich f\u00fcr ein Auto den Preis vorhersagen zu lassen. Die Eigenschaften werden (wie im nachfolgenden Screenshot abgebildet) im JSON-Format im Body des POST-Request mitgegeben.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9b28b65 elementor-widget elementor-widget-image\" data-id=\"9b28b65\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"513\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Using-REST-API-for-Prediction-1024x513.png\" class=\"attachment-large size-large wp-image-15988\" alt=\"\" srcset=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Using-REST-API-for-Prediction-1024x513.png 1024w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Using-REST-API-for-Prediction-300x150.png 300w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Using-REST-API-for-Prediction-768x385.png 768w, https:\/\/www.cubeserv.com\/wp-content\/uploads\/2020\/07\/SAP-Data-Intelligence-Using-REST-API-for-Prediction.png 1196w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5222a1a elementor-widget elementor-widget-heading\" data-id=\"5222a1a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Integration von Machine Learning Pipelines in Ihre IT-Systemlandschaft<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2435822 elementor-widget elementor-widget-text-editor\" data-id=\"2435822\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Wie kann eine Machine Learning Pipeline mit einer graphischen Benutzeroberfl\u00e4che in Ihrer SAP Systemlandschaft erstellt und voll-integriert angewendet werden? In diesem Blog-Beitrag habe ich Ihnen gezeigt, wie dies mit SAP Data Intelligence m\u00f6glich ist.&nbsp;<\/p>\n<p>Des Weiteren ist es durch die Verwendung der Jupyter Notebooks mit Python einfach m\u00f6glich die sehr iterative Vorgehensweise bei Data Mining oder Data Science Vorhaben integriert umzusetzen und einfach in bestehende Gesch\u00e4ftsprozesse und IT-Prozesse zu integrieren und dadurch einen Mehrwert zu bieten.<\/p>\n<p>Gerne tausche ich und meinen Kollegen\/innen mich mit Ihnen \u00fcber Ihre Data Science Vorhaben aus und wir pr\u00fcfen inwieweit wir Sie in Zukunft unterst\u00fctzen k\u00f6nnen.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-48d312b elementor-section-height-min-height elementor-section-content-top elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"48d312b\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bc0bf31\" data-id=\"bc0bf31\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-3a160b9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3a160b9\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-ae4c325\" data-id=\"ae4c325\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-f4abdec elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f4abdec\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-c27b293\" data-id=\"c27b293\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-05fae66 elementor-headline--style-highlight elementor-widget elementor-widget-animated-headline\" data-id=\"05fae66\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;marker&quot;:&quot;underline&quot;,&quot;highlighted_text&quot;:&quot;Expert Call.&quot;,&quot;headline_style&quot;:&quot;highlight&quot;,&quot;loop&quot;:&quot;yes&quot;,&quot;highlight_animation_duration&quot;:1200,&quot;highlight_iteration_delay&quot;:8000}\" data-widget_type=\"animated-headline.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<h3 class=\"elementor-headline\">\n\t\t\t\t\t<span class=\"elementor-headline-plain-text elementor-headline-text-wrapper\">Vereinbaren Sie jetzt Ihren<\/span>\n\t\t\t\t<span class=\"elementor-headline-dynamic-wrapper elementor-headline-text-wrapper\">\n\t\t\t\t\t<span class=\"elementor-headline-dynamic-text elementor-headline-text-active\">Expert Call.<\/span>\n\t\t\t\t<\/span>\n\t\t\t\t\t<span class=\"elementor-headline-plain-text elementor-headline-text-wrapper\">Wir freuen uns \u00fcber Ihre Nachricht.<\/span>\n\t\t\t\t\t<\/h3>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-224a77c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"224a77c\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-0e1b825\" data-id=\"0e1b825\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1fe4646 elementor-author-box--image-valign-middle elementor-author-box--avatar-yes elementor-author-box--link-no elementor-widget elementor-widget-author-box\" data-id=\"1fe4646\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"author-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-author-box\">\n\t\t\t\t\t\t\t<div  class=\"elementor-author-box__avatar\">\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/www.cubeserv.com\/wp-content\/uploads\/2021\/05\/Benedikt_Bleyer.png\" alt=\"Picture of Benedikt Bleyer\" loading=\"lazy\">\n\t\t\t\t<\/div>\n\t\t\t\n\t\t\t<div class=\"elementor-author-box__text\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-4ab316b\" data-id=\"4ab316b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c33d9f2 elementor-widget elementor-widget-spacer\" data-id=\"c33d9f2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ee421ac elementor-widget elementor-widget-heading\" data-id=\"ee421ac\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Benedikt Bleyer<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a383d91 elementor-widget elementor-widget-heading\" data-id=\"a383d91\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Design, Build and Run Your Business Analytics Platform. Professional experience in Advanced Analytics, Data Integration, Data Governance &amp; Enterprise Planning.<\/h5>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-f307057 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f307057\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-df61b91\" data-id=\"df61b91\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e4c2a99 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"e4c2a99\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"tel:+41552243000\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fas-phone-alt\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M497.39 361.8l-112-48a24 24 0 0 0-28 6.9l-49.6 60.6A370.66 370.66 0 0 1 130.6 204.11l60.6-49.6a23.94 23.94 0 0 0 6.9-28l-48-112A24.16 24.16 0 0 0 122.6.61l-104 24A24 24 0 0 0 0 48c0 256.5 207.9 464 464 464a24 24 0 0 0 23.4-18.6l24-104a24.29 24.29 0 0 0-14.01-27.6z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">+41 55 224 30 00<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"mailto:benedikt.bleyer@cubeserv.com\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-far-envelope\" viewBox=\"0 0 512 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\"><\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-inner-column elementor-element elementor-element-ac7e4f2\" data-id=\"ac7e4f2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7109f87 elementor-icon-list--layout-traditional elementor-list-item-link-full_width elementor-widget elementor-widget-icon-list\" data-id=\"7109f87\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"icon-list.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul class=\"elementor-icon-list-items\">\n\t\t\t\t\t\t\t<li class=\"elementor-icon-list-item\">\n\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.linkedin.com\/in\/benedikt-bleyer\">\n\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-icon\">\n\t\t\t\t\t\t\t<svg aria-hidden=\"true\" class=\"e-font-icon-svg e-fab-linkedin\" viewBox=\"0 0 448 512\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg>\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-icon-list-text\">benedikt-bleyer<\/span>\n\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t<\/li>\n\t\t\t\t\t\t<\/ul>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Wie kann eine Machine Learning Pipeline mit einer graphischen Benutzeroberfl\u00e4che in Ihrer SAP Systemlandschaft erstellt und voll-integriert angewendet werden? In diesem Blog-Beitrag zeige ich Ihnen wie es geht. Die Screenshots wurden im Rahmen eines Workshops bei der Swiss Data Science Conference 2020 mit SAP Data Intelligence Version 3.0 erstellt. Als erster Use Case wurde die &#8230; <a title=\"Integration von Machine Learning Pipelines in die SAP Systemlandschaft\" class=\"read-more\" href=\"https:\/\/www.cubeserv.com\/de\/integration-von-machine-learning-pipelines-in-die-sap-systemlandschaft\/\" aria-label=\"Mehr Informationen \u00fcber Integration von Machine Learning Pipelines in die SAP Systemlandschaft\">Weiterlesen &#8230;<\/a><\/p>\n","protected":false},"author":14,"featured_media":5998,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[508,1,511],"tags":[515,518,525,512,513,514,516,407],"class_list":["post-15927","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-analytics","category-sap-data-hub","category-sap-data-intelligence","tag-boosting","tag-crisp-dm","tag-jupyter-notebook","tag-machine-learning","tag-python","tag-regression","tag-sap-data-intelligence","tag-sap-hana"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v27.5) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Integration von Machine Learning Pipelines in die SAP Systemlandschaft - CubeServ<\/title>\n<meta name=\"description\" content=\"Wie kann eine Machine Learning Pipeline mit einer graphischen Benutzeroberfl\u00e4che in Ihrer SAP Systemlandschaft erstellt und voll-integriert angewendet werden? 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