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: Operationalizing Python and R with the Pipeline Modeler