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Author's profile photo Leo Jacinto Francia

SAP AI and ML Tools – when to use what, insights 2022 edition

After being an SAP technical consultant for more than 10 years, serving various industries; and having done presales for over 3 years, I explored Artificial Intelligence and Machine Learning (AI/ML). Having been an AI/ML Catalyst in SAP’s 2022 cohort during my time there, I had access to various training materials as well as product and subject matter experts globally within SAP.

In my previous LinkedIn post, I shared a simple decision tree on when to use which SAP tool for AI/ML purposes (included here for convenience).Decision tree on SAP AI/ML tools

This blog might be useful if any of these apply to you:

  1. You are a data/business analyst or an SAP practitioner looking at ways to improve and automate decision making
  2. You have acceptable data quality levels, e.g., based on business and technical team assessments
  3. You have your data sources ready, with teams agreeing on who will do what in which tool to make the data ready for consumption. I recommend checking out Peter Baumann‘s blog series on data architectures starting here.
  4. You have a business problem or use case in mind
  5. You are deciding which SAP AI/ML tool to apply to data with (presumably) a large SAP footprint

Now, onto the table. Having used these tools in the last 2 years, there are areas that I have not explored comprehensively yet. Eager to hear your thoughts and experience.

Tool Name / Attribute What the attribute means SAP Data Intelligence SAP Al Core and Launchpad SAP Al Business Services SAP Analytics Cloud Predictive Scenarios SAP Embedded Intelligent Scenario Lifecycle Management (ISLM) Hyperscaler ML platforms (Azure ML, AWS Sagemaker, GCP ML, Databricks, etc.)
What the tool is for Big Data orchestration and pipelining Base AI/ML integration and operations platform For different use cases, see here Analytics tool with predictive capabilities Operate machine learning scenarios within S/4HANA Big Data orchestration and pipelining + Machine Learning
Suggested link for initial reading SAP Data Intelligence: Self-learning resources SAP AI Core & Launchpad Introduction SAP AI Business Services Your first Predictive Scenario in SAC Brief Introduction to ML capabilities in S/4HANA Federated Machine Learning Libraries for Hyperscalers
Development environment Where you perform the creation of data pipelines, models, etc. SAP Data Intelligence, Jupyter Notebook SAP BTP SAP BTP SAP Analytics Cloud SAP S/4HANA, SAP BTP, 3P libraries (e g. via Jupyter Notebook) Jupyter Notebook, platform-specific
3P libraries Third-party libraries like pandas, NumPy, sci-kit learn, etc. Yes Yes No No No Yes
Container support Allows build, test, and deployment of packaged images that runs in any environment Yes Yes No No No Yes
Native SAP integration Preconfigured option to connect to SAP data sources Yes Yes Yes Yes Yes No
Job scheduling or orchestration Automation and monitoring of data flow, from transformation to final output Yes Yes No Yes No Yes
Pipelining Moving data from one place to another Yes Yes No No Not applicable Yes
Pre-processing Manipulation or transformation of data Yes Yes (using libraries) No No (limited) Not applicable Yes
Model training Using new data for an algorithm to learn and make predictions Yes (using libraries) Yes (using libraries) Yes Yes Yes Yes
Classification Ordering or categorization of data into one or more “classes” (e.g., Spam vs Not Spam) Yes (using libraries) Yes (using libraries) Yes Yes Yes Yes
Regression Predicts continuous values based on variable relationships Yes (using libraries) Yes (using libraries) Yes Yes Yes Yes
Clustering Grouping of unlabelled examples Yes (using libraries) Yes (using libraries) Yes No No Yes
Natural Language Processing Breaking down and interpretation of human language Yes (using libraries) Yes (using libraries) Yes No No Yes
Neural network Processing of data like the human brain, learning through trial-and-error Yes (using libraries) Yes (using libraries) Yes No No Yes
AutoML Automation of repetitive tasks when building an ML model Yes (using libraries) Yes (using libraries) Yes Yes No Yes
Batch model serving (inference) Making ML functions available via reports Yes (using libraries) Yes Yes Yes Yes Yes
Online model serving (inference) Making ML functions available via API Yes (using libraries) Yes Yes No Yes Yes
MLOps Efficient and reliable deployment and maintenance of ML models Yes Yes Yes Yes No Yes

Note: “Using libraries” means the feature is not native and you will use libraries in tools such as Jupyter Notebook to make the feature available in your deployment. The table above does not include details on image classification capabilities as of now, as I have not yet personally come across use cases beyond structured and unstructured text.

Once you have identified the most suitable tool for your use case, I suggest considering the steps below before proceeding with a full-blown project (Mark Muir has a blog about this):

  1. Review the tool prerequisites
  2. Review the licensing
  3. Integrate the AI/ML tool into your pipeline
  4. Design your AI/ML experiment and test approach

In summary, there are different approaches you can consider on how to deploy AI/ML in your SAP landscape depending on your requirements. I hope it gives you a starting point in exploring the AI/ML tools available. The applicability of the information in the table above will likely change (and quickly) over time. To stay up to date, you can follow pages such as:

Would be great to hear about your thoughts and experience using these tools in the comments section.


Invariably stochastically yours,


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      Author's profile photo Antoine CHABERT
      Antoine CHABERT

      Hi Leo Jacinto Francia | SAP People thanks for you blog. As the SAP Analytics Cloud product manager I would just bring your attention to two points:

      • we do have job scheduling / orchestration when it comes to predictive planning (time series forecasting on planning models) since Q1 2022, please do refer to the multi action framework blogs here: . This framework is expanding very fast and will offer REST APIs integration in Q4 2022 as an example.
      • By definition & design SAP Analytics Cloud predictive scenarios are based on an AutoML approach. As an example our time series forecasting approach puts in competition dozens of candidate models behind the scenes.

      Kind regards,

      Antoine Chabert

      SAP Analytics Cloud Product Manager

      Author's profile photo Leo Jacinto Francia
      Leo Jacinto Francia
      Blog Post Author

      Hi, Antoine CHABERT, it is great to hear from you, and thank you for your note! Not sure if you remember our brief collaboration on our renewal prediction last year. I have updated the blog based on the two points you mentioned. The addition of new features is very encouraging news!

      Author's profile photo Antoine CHABERT
      Antoine CHABERT

      Of course, I do remember it Leo 🙂 Thanks for the swift answer & taking my points into account. Best, Antoine