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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.
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Drive Predictive planning through machine learning with SAP Analytics CloudJun 7, 2021 — SAP Analytics Cloud provides machine learning capabilities that can help organizations integrate advanced and predictive analytics within their planning processes. Given the rapid pace of change and disruption within global organizations, these features can provide useful insight to the business. In this session you will:
- Gain a detailed introduction to the concept of predictive planning and how it is improving the way enterprise organizations run their business
- Walk through the machine learning and predictive planning capabilities available in SAP Analytics Cloud
- Understand the skills and technology prerequisites needed to unlock the potential of SAP Analytics Cloud’s predictive planning capabilities
1 minute read
Leveraging Machine Learning (ML) in Spend Analysis solutionsJan 25, 2021 — Machine Learning (ML) based algorithms are slowly percolating into a wide variety of supply chain solutions and spend analysis solutions are no exception. In this article, I share few ways Machine Learning (ML) algorithms are already being used or can be used in spend analysis context. Note that these suggestions are focused only on spend […]
3 minute read
Machine Learning in the Financial Close Process: Where can it add the most value?Oct 20, 2020 — This session explores how utilizing Machine Learning can help automate many of the day-to-day tasks that can often consume the finance organization, helping companies shift focus from a transactional to a more strategic approach to finance. Topics include:
- An overview of Machine Learning (what it is, how does it work)
- An overview of Predictive Accounting and how it can help transform the way in which an organization takes on the accounting process
- Finance Scenarios where ML can add value
1 minute read
SAP Leonardo Vs. TensorFlowJul 7, 2020 — Machine learning (ML), considered a subset of artificial intelligence, is growing in popularity among businesses that want to create better computing models with high volumes of data in order to make decisions faster. SAP Leonardo and Google’s TensorFlow are both known, in part, for their ability to prototype and integrate with ML projects. In this article, the author compares the subtle differences between the two solutions to help customers determine which utility will meet their individual ML project needs, including real-life examples that demonstrate how two well-known companies are using each product.
After reading this article you will be able to:
- State several similarities between SAP Leonardo and TensorFlow;
- Identify specific differences between the two solutions; and
- Understand which components might make one solution the best option for your ML project.
4 minute read
Bring the Power Of Machine Learning Directly To Business UsersFeb 27, 2020 — A new wave of disruption is hitting the analytics market: augmented analytics. Machine learning infused in business intelligence and planning workflows helps users make decisions with confidence – without IT intervention or data science training. Read this brochure to learn how to bring the power of machine learning directly to business users.
1 minute read
5 Important Announcements for SAPinsidersSep 27, 2019 — SAP TechEd took place this week in Las Vegas, unveiling several developments for the SAPinsider community – not the least of which being SAP’s and Microsoft’s joint announcement that Microsoft Azure Blockchain Service and SAP Cloud Platform Blockchain Service will be interoperable. During his main keynote, which outlined SAP’s business technology platform strategy for the […]
3 minute read
Big Data and Machine Learning Tools Flag Fake News and Build Trust and TransparencyJun 25, 2018 —
To be successful, companies must operate with complete transparency and gain the trust of employees and customers alike. But with fake news running rampant, it can be difficult to sift through the noise and get to the facts.
3 minute read
The Future of Finance and Risk ManagementFeb 28, 2018 — Digitization is changing the business environment on a foundational level, and new technologies are providing opportunities to profoundly reimagine finance and risk management. New developments like machine learning and blockchain will pave the way to the truly intelligent enterprise, while those resistant to change will be left behind. In this new landscape, what will the […]
1 minute read
Deploying Machine Learning to Build an Intelligent EnterpriseJan 18, 2018 —
An absence of strategy trails only domain expertise as the reason why many companies have yet to adopt artificial intelligence and machine learning. Companies are challenged with how to apply these and other breakthrough technologies for business value. This article details several examples of how organizations can deploy machine learning today, and how SAP S/4HANA and SAP Leonardo come together in the cloud and on-premise to address the many innovative opportunities the technology brings.
4 minute read