{"id":37110,"date":"2022-06-09T10:01:50","date_gmt":"2022-06-09T08:01:50","guid":{"rendered":"http:\/\/54.194.80.134.nip.io\/integration-of-machine-learning-pipelines-into-the-sap-system-landscape\/"},"modified":"2022-09-27T14:57:03","modified_gmt":"2022-09-27T12:57:03","slug":"integration-of-machine-learning-pipelines-into-the-sap-system-landscape","status":"publish","type":"post","link":"https:\/\/www.cubeserv.com\/en\/integration-of-machine-learning-pipelines-into-the-sap-system-landscape\/","title":{"rendered":"Integration of Machine Learning Pipelines into the SAP System Landscape"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"37110\" class=\"elementor elementor-37110 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\">\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\">\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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHow can a machine learning pipeline with a graphical user interface be created in your\r\nSAP system landscape and apply it in a fully integrated way? In this blog post, I will show you how.\r\n\r\nThe screenshots were taken during a workshop at the Swiss Data Science Conference 2020 using SAP Data Intelligence version 3.0.\r\n\r\nThe first use case chosen was the price prediction of cars. The corresponding data set can be found on Kaggle, see <a href=\"https:\/\/www.kaggle.com\/bozungu\/ebay-used-car-sales-data\">https:\/\/www.kaggle.com\/bozungu\/ebay-used-car-sales-data<\/a>. The value to be predicted (output variable) is the price in Euros for which a used car was offered in 2016. The data is stored in an SAP HANA database, which ensures in-memory access and thus high performance.\r\n\r\nThe process model used is based on the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. The first four phases (business understanding, data understanding, modelling and evaluation) were first implemented with the support of Jupyter Notebook using Python. Subsequently, the parameter configuration of the gradient boosting regression algorithm with the smallest root means squared error (RMSE) was used to model the machine learning pipeline in the graphical user interface of SAP Data Intelligence and deployed as a RESTful API in the last step.\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-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 with 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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tIn the &#8220;data understanding&#8221; phase, the use of Jupyter Notebooks in the Python programming language offers a variety of functionalities for analysis and the corresponding preparation of the results. The following screenshot shows different values for the columns in the used dataset, e.g. the uniqueness of the values, zero values as well as average and median values. This makes it possible, for example, to find out whether an outlier treatment is necessary and how the data set can be sensibly divided into training and validation data sets.\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-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-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 with 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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAfter getting to know the data, the choice of the algorithm to be used fell on the Hybrid Gradient Boosting Regression from SAP Predictive Analytics Library (SAP PAL). The parameters entered were chosen as the starting configuration, and then the columns or features to be used were specified in order to create a variety of training models in the last step. For technical and mathematical detailed information about Gradient Boosting in Python, I can recommend the following website: <a href=\"https:\/\/towardsdatascience.com\/gradient-boosting-in-python-from-scratch-4a3d9077367\">Gradient Boosting in Python from Scratch<\/a>\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-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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tSubsequently, it is possible to evaluate the quality of the models according to various statistical key figures. In the screenshot below, the Root-Mean-Square Error (RMSE) was chosen as the metric. The parameters (N_ESTIMATORS and MAX_DEPTH) that led to the smallest RSME were chosen to train the model to be used.\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-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-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>Using Parameter Configuration in SAP Machine Learning Operator for In-Memory Execution<\/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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAfter modelling and evaluation, I have often experienced in the past that it is a big challenge for companies to deploy the developed and evaluated models into production. The integration into existing business processes as well as managing the machine learning pipeline across different IT systems (with different programming languages) were also among the sometimes underestimated activities. The Data Intelligence Modeler allows this integration of IT systems and programming languages in a graphical user interface. The screenshot below shows the data pipeline used to train the strongest model.\r\n\r\nSince modelling and evaluation in CRSIP-DM are often done in several iterations, it makes sense and is helpful to do this in Jupyter Notebook. Afterwards, the parameters that led to the strongest model can be passed to the standard component &#8220;HANA ML Training&#8221;.\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-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%3DeyJpZCI6IjE1OTM1IiwidXJsIjoiaHR0cHM6XC9cL3d3dy5jdWJlc2Vydi5jb21cL3dwLWNvbnRlbnRcL3VwbG9hZHNcLzIwMjBcLzA3XC9TQVAtTWFjaGluZS1MZWFybmluZy1PcGVyYXRvci5wbmcifQ%3D%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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tDifferent operators can be used in each pipeline in the Data Intelligence Modeller. An example is the &#8220;HANA ML Training&#8221;. However, the Python operator can also be used to integrate individual Python code or R code (e.g. if an algorithm from SAP PAL is not to be used, as in this example).\r\n\r\nAccess to a variety of databases, Cloud providers and other SAP products such as S\/4HANA is possible as well as the control of SAP BW process chains.\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-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 of 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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tOnce the strongest model has been created, it can now be made available in a consumer pipeline via a RESTful API, as shown in the screenshot below.\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-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-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\">Call RESTful API for price prediction based on Machine Learning model.<\/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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAfter successful deployment, it is possible to use the interface with a POST request to get a price prediction for a car. The properties are provided in JSON format in the body of the POST request (as shown in the screenshot below).\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-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-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 of machine learning pipelines into your IT system landscape<\/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-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHow can a Machine Learning pipeline with a graphical user interface be created in your SAP system landscape and applied in a fully integrated way? I showed you how this is possible with SAP Data Intelligence in this blog post.\r\n\r\nFurthermore, using Jupyter Notebooks with Python makes it easy to implement the very iterative approach to Data Mining or Data Science projects in an integrated way and easily integrate it into existing business processes and IT processes to provide added value.\r\n\r\nMy colleagues and I would be happy to discuss your data science projects with you and to check how we can support you in the future.\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-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-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\">\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\">\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-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\">\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-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\">Arrange now your<\/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\">We are glad to hear from you.<\/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-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\">\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-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\">\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-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-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-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-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\">\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-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=\"\">\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\">\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-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>How can a machine learning pipeline with a graphical user interface be created in your SAP system landscape and apply it in a fully integrated way? In this blog post, I will show you how. The screenshots were taken during a workshop at the Swiss Data Science Conference 2020 using SAP Data Intelligence version 3.0. &#8230; <a title=\"Integration of Machine Learning Pipelines into the SAP System Landscape\" class=\"read-more\" href=\"https:\/\/www.cubeserv.com\/en\/integration-of-machine-learning-pipelines-into-the-sap-system-landscape\/\" aria-label=\"Read more about Integration of Machine Learning Pipelines into the SAP System Landscape\">Read more<\/a><\/p>\n","protected":false},"author":14,"featured_media":6000,"comment_status":"closed","ping_status":"closed","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-37110","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 v24.6) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Integration of Machine Learning Pipelines into the SAP System Landscape - CubeServ<\/title>\n<meta name=\"description\" content=\"How to create a Machine Learning Pipeline with a graphical user interface in your SAP system landscape and apply it in a fully integrated way? 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