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Machine Learning Features in SAP Products
What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) algorithms. The differentiating aspect of these algorithms is that they can learn from the input data and modify the model based on changes in that data. It is this “learning” aspect that makes these algorithms powerful.
Machine Learning Applications in SAP Portfolio
SAP applications leverage ML algorithms extensively to embed innovative capabilities within their solutions, help end-users perform advanced analytics with minimal technical proficiency, and allow data scientists and ML engineers to build advanced models and solutions. Below are some examples:
- SAP HANA
SAP HANA has been designed to be easily leveraged as a scalable ML platform. A powerful built-in tool is the predictive analytics library (PAL). A component of the application function library in HANA, PAL includes several algorithms to enable the most frequently used predictive analytics use cases. For advanced users who want to explore advanced algorithms like deep learning, extended machine library (EML) in HANA allows such users to leverage TensorFlow to build deep learning algorithms.
- SAP Data Intelligence
SAP data intelligence has a rich ML content library. This library, which has an ML scenario manager and ML operations cockpit, allows engineers and data scientists to collaborate and build ML models.
- SAP Analytics Cloud Smart Predict
Like most best-of-breed analytics tools, SAP Analytics Cloud provides users the ability to leverage advanced ML algorithms. While ML algorithms have many applications, predictive analytics remains a key one.
Key Considerations for SAPinsiders
Machine Learning Features in SAP Products
What is Machine Learning?
Machine learning (ML) is a subset of artificial intelligence (AI) algorithms. The differentiating aspect of these algorithms is that they can learn from the input data and modify the model based on changes in that data. It is this “learning” aspect that makes these algorithms powerful.
Machine Learning Applications in SAP Portfolio
SAP applications leverage ML algorithms extensively to embed innovative capabilities within their solutions, help end-users perform advanced analytics with minimal technical proficiency, and allow data scientists and ML engineers to build advanced models and solutions. Below are some examples:
- SAP HANA
SAP HANA has been designed to be easily leveraged as a scalable ML platform. A powerful built-in tool is the predictive analytics library (PAL). A component of the application function library in HANA, PAL includes several algorithms to enable the most frequently used predictive analytics use cases. For advanced users who want to explore advanced algorithms like deep learning, extended machine library (EML) in HANA allows such users to leverage TensorFlow to build deep learning algorithms.
- SAP Data Intelligence
SAP data intelligence has a rich ML content library. This library, which has an ML scenario manager and ML operations cockpit, allows engineers and data scientists to collaborate and build ML models.
- SAP Analytics Cloud Smart Predict
Like most best-of-breed analytics tools, SAP Analytics Cloud provides users the ability to leverage advanced ML algorithms. While ML algorithms have many applications, predictive analytics remains a key one.
Key Considerations for SAPinsiders
- Develop a fundamental understanding of algorithms: Explore what specific algorithms are available and understand where they can be leveraged. This will help you get optimal value from these tools. As an example, you should be aware that you can use clustering algorithms for customer segmentation. Here is an example of a good overview of critical algorithms used in SAP applications.
- Understand the limitations of underlying data infrastructure: Understanding aspects of the underlying database is also critical. This helps you build pragmatic models. As an example, HANA has a 2 billion rows limitation, and hence you may have to partition tables for data sets larger than that. This impacts your model development as well.
- Understand the limitations of tools available: Some PAL algorithms have limits on the number of parameters. This means you will have to pay more attention to feature selection or feature engineering while building models with these algorithms. You can find several examples of these limitations on the SAP help portal and SAP blogs.
29 results
The CFO’s AI Playbook: Unleashing Next-Gen Financial Operations with Generative AI & ML in SAP S/4HANAAug 21, 2025 — The evolving role of the CFO now emphasizes strategic value creation through AI and ML integration in financial operations, as outlined in The SAP CFO’s AI Playbook, which guides leveraging these technologies within SAP S/4HANA to enhance efficiency, decision-making, and consultant responsibilities in an increasingly automated landscape.
6 minute read
Understanding and Maximizing Large Language Models (LLMs)Jan 16, 2025 — As organizations embrace the Age of AI, particularly through the use of Large Language Models (LLMs) for tasks like translation and summarization, effective implementation requires fine-tuning and prompt engineering to maximize productivity and ensure high-quality outputs.
2 minute read
How AI Can Be Utilized to Achieve Seamless Data Integration ExperienceFeb 26, 2024 — As time progresses, so does technological advancement. Artificial Intelligence (AI) is gradually becoming a natural actor in businesses’ lives, overtaking various types of operations and transforming legacy systems. Among those operations stands progress in a faster, easier, and more efficient way of integrating data across SAP systems. For years organizations have used a traditional way […]
1 minute read
Super-Powering Your Factory TransformationJun 28, 2023 — Leverage intelligent manufacturing solutions from Fujitsu to drive sustainable growth while saving energy, reducing waste, and minimizing your climate footprint. From initial stages to advanced initiatives, Fujitsu, in collaboration with Microsoft Azure and SAP, stands ready to be your trusted partner in creating new value for your business, society, and the environment. Don't miss this exclusive opportunity to gain insights from industry experts, analysts, and thought leaders, and discover how the integration of Microsoft Azure, SAP, and Fujitsu's smart manufacturing solutions can shape a more sustainable future for manufacturing.
1 minute read
Machine Learning Renaissance with SAP Kyma 2.0Jun 22, 2022 — Open source development is very normal in the data science world. The popularity of languages like Python and R can be attributed to the explosion of development work happening to leverage open-source tools and technologies. However, many who are on SAP technologies are often unsure how to leverage and integrate open source tools within their SAP technology portfolio. In this video, SAPinsider analyst Kumar Singh provides his perspectives on how SAP Kyma opens a floodgate of opportunities who want to exploit machine learning to its full potential in their SAP environments.
1 minute read
Automated Machine Learning in the CloudOct 28, 2021 — Automated machine learning tools have evolved into the democratization of data science. They now include a broader scope, encompassing the automation of the entire data-to-insights pipeline — from cleaning data to tuning algorithms through feature selection and creation, even operationalization. And now, with the advent of the cloud, the case of AutoML has become much more substantial. This article will explore:
Why the cloud has fueled the need for Automated ML
What are the Automated ML solutions offered by hyperscalers
What are some critical aspects SAPinsiders need to be aware of when strategizing about Automated ML in the cloud
8 minute read
Machine Learning Powered Intelligent Replenishment in RetailJul 6, 2021 — by Kumar Singh, Research Director, Automation & Analytics, Supply Chain Management, SAPinsider The criticality of establishing an efficient store replenishment process The process efficiency of replenishing store inventory is critical to a retailer’s overall operating efficiency and even profitability. It is not news to anyone that store replenishment impacts the on-the-shelf availability. In today’s […]
4 minute read