SAP Machine Learning


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.

23 results

  1. SAP Kyma

    Machine Learning Renaissance with SAP Kyma 2.0

    Reading time: 1 min

    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…
  2. Kumar Singh -- Image

    Automated Machine Learning in the Cloud

    Reading time: 8 mins

    It has been more than eight years since AutoWEKA, the first free and open-source machine learning library, was released in 2013. It is not surprising then that automation of the data science process has been around for nearly a decade, but these tools have evolved extensively in recent years. The initial technologies were mainly focused…
  3. Vodaphone image

    Machine Learning Powered Intelligent Replenishment in Retail

    Reading time: 4 mins

    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. Drive Predictive planning through machine learning with SAP Analytics Cloud

    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…
  5. Leveraging Machine Learning (ML) in Spend Analysis solutions

    Reading time: 3 mins

    by Kumar Singh, Research Director, Automation & Analytics, SAPinsider   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…
  6. Machine Learning in the Financial Close Process: Where can it add the most value?

    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…
  7. SAP Leonardo Vs. TensorFlow

    Reading time: 4 mins

    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…
  8. Bring the Power Of Machine Learning Directly To Business Users

    Reading time: 0 min

    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.
  9. 5 Important Announcements for SAPinsiders

    Reading time: 3 mins

    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…
  10. Big Data and Machine Learning Tools Flag Fake News and Build Trust and Transparency

    Reading time: 3 mins

    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. This content is available to Premium Members. Sign In Learn more about Premium Access