With the recent breakthroughs in artificial intelligence (AI), many companies are pursuing the means to apply machine learning-based techniques to their business processes to transform and improve their usability and profitability and accelerate industry growth. SAP aspires to make all its enterprise solutions smart and help customers evolve to an intelligent enterprise.
This course offers an introduction to SAP Data Intelligence, SAP’s new AI/data science platform to manage complex data landscapes, build scalable data pipelines, and provision the entire data science process from proof of concept development to operationalization, continuous optimization, and adaptation. SAP Data Intelligence is a flexible solution that connects open source environments like JupyterLab with proven SAP technologies like SAP HANA and SAP Leonardo Machine Learning, while allowing you to work across them seamlessly. The features offered facilitate the building of smart applications for customers and business partners.
In this course, we’ll discuss use cases for enterprise machine learning applications. We’ll show you how to work with popular languages, such as Python and R, or your favorite libraries such as TensorFlow, in a development to production environment that supports you through the entire lifecycle management, from data access to continuous model retraining and deployment. You’ll also go through a variety of demos to learn how to build and consume your own machine learning/deep learning models.
The course is aimed mainly at data science enthusiasts but is also suitable for anyone interested in data science and innovation, focusing on the specific product capabilities for developing a data science scenario in an enterprise landscape. To learn more about the data management aspects of SAP Data Intelligence for data engineers, developers, and development operations, we highly recommend you also visit the course Freedom of Data with SAP Data Hub (HUB1) on openSAP.
Unit 1: Enabling the Intelligent Enterprise with Machine Learning
Unit 2: Intelligent SAP Applications
Unit 3: Customer Use Cases
Unit 4: SAP Data Intelligence Capabilities for Data Scientists
Unit 5: SAP Data Intelligence Launchpad and Components
Unit 6: Machine Learning Scenario Manager
Unit 7: Data Science Experiments in Jupyter Notebook (PAL, APL, Python)
Unit 8: Working with the SAP Data Intelligence Pipeline Modeler
Unit 9: Machine Learning in Native Python and R
Unit 10: Intelligent Services
Unit 11: Summary and Outlook
Dr. Matthias Sessler is a Product Expert at SAP and leads the technical enablement for SAP Data Intelligence. His vision is to enable customers, partners, and internal development teams to make enterprise applications intelligent.
Matthias has more than 15 years of experience in the SAP technology and SAP Leonardo Machine Learning area. He previously held several positions in presales, software development, and product management. In addition, he worked as a lecturer in digital technology and computer architecture at DHBW Mosbach.
With his academic background as physicist, one of his main focuses are the Data Science capabilities of SAP Data Intelligence. Matthias earned his PhD at CERN in collaboration with the University of Heidelberg, where he developed pattern recognition algorithms for finding the Higgs boson.
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Puntis Palazzolo is a Sr. Data Scientist at SAP where she manages the SAP Big Data solution, SAP Data Hub, Developer-focused and Advanced Analytics topics in her role in SAP Data Hub/SAP Data Intelligence Product Management team. She has more than a decade of experience in software design and development, machine learning systems and database technologies in different industries such as Bioinformatics, Military and Health Care and applications such as Handwriting and Voice Recognition, Image Processing, Natural Language Processing and Recommendation Engines.
Puntis has several research publications in the field of Machine Learning and Data Science and has patented ideas in the field of Recommendation Engines.
Her academic background is in Computer and Electrical Engineering, Computer Science and Software Engineering.