Hello, I am a dual student at SAP studying International Business Administration and Information Technology.
In this blog, I will happily share with you the data science project I undertook during my practical phase as part of my studies. The practical phase was supervised by the Industry Solution Management for Energy and Utilities Industries – Contact Raik Kulinna.
After detecting outliers with the power of Python tools (Blog: Detecting Implausible User-Entered Weighing Notes using Data Science with an SAP S/4HANA (On-Premise) System) as well as the advanced SAP tools for data science (Blog: Data Science with SAP HANA Predictive Analytics Library (PAL), AMDP (ABAP Managed Database Procedure) and Core Data Services (CDS) Table Functions), I am going to explore in this blog another SAP Solution in the world of data science, called Intelligent Scenario Lifecycle Management (ISLM).
SAP Intelligent Scenario Lifecycle Management (ISLM) is a framework developed by SAP where you can manage artificial intelligence (AI) operations directly inside SAP S/4HANA with real-time access of all operational data. ISLM is a tool that allows users to train data science scenarios and use the trained model to obtain accurate inference results. Depending on the associated business application, the machine learning scenarios can be categorized as Embedded or Side-by-Side.
In the Embedded approach, the business application, such as SAP S/4HANA, runs in the same stack as its machine learning provider, SAP HANA, using analytics libraries like SAP HANA Automated Predictive Library (APL) or SAP HANA Predictive Analysis Library (PAL).
In the Side-by-Side approach, the business application runs in a separate stack from its machine learning infrastructure provider, such as SAP Data Intelligence. This approach is used for high-end use cases like image recognition, sentimental analysis, and deep learning for natural language processing.
In this blog, we will explore the embedded approach on top of the industry solution specific data inside SAP S/4HANA for Waste and Recycling.
SAP ISLM contains two SAP Fiori applications, Intelligent Scenarios, and Intelligent Scenario Management, that allow users to create and manage the lifecycle of intelligent scenarios. The functionalities of ISLM include viewing the details of intelligent scenarios, creating intelligent scenarios, training scenarios to create a machine learning model, deploying trained models for inference consumption, activating trained models to get inference results, and obtaining predictions created from trained models.
SAP provides an SAP documentation where you can learn more about ISLM and where you can get a set of generic instructions to get started with ISLM. You can always refer to the following documentation for further support Introduction to Intelligent Scenario Lifecycle Management.
In the following, I will walk you through the steps I needed to go through to be able to use the embedded approach of SAP ISLM.
If you want a more detailed set of instructions on how to create and publish an Intelligent Scenario, you can go to this link Introduction to Intelligent Scenario Lifecycle Management and look for the section you seek help on.
You should now be able to see the prediction results of your Intelligent Scenario. In my case, the result is a list of predicted outliers that looks like the following:
Incorporating outlier detection into your business processes is crucial for making informed decisions. SAP Intelligent Scenario Lifecycle Management (ISLM) offers a valuable solution that not only helps identifying outliers but also provides actionable insights, enhancing the overall efficiency of your operations. By leveraging SAP ISLM’s capabilities, organizations can stay ahead of potential issues and optimize their processes for greater success in today’s competitive landscape. It’s worth to check it for the wide range of data science tasks for the wide range of processes inside SAP S/4HANA.