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).
This blog might be useful if any of these apply to you:
- You are a data/business analyst or an SAP practitioner looking at ways to improve and automate decision making
- You have acceptable data quality levels, e.g., based on business and technical team assessments
- 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.
- You have a business problem or use case in mind
- 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.
- Review the tool prerequisites
- Review the licensing
- Integrate the AI/ML tool into your pipeline
- 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:
- SAP AI Core environment topic page
- SAP AI Core questions
- Other posts on SAP AI core
- And my profile, Leo Jacinto Francia | SAP People, for upcoming posts on Data Science, AI, and ML
Would be great to hear about your thoughts and experience using these tools in the comments section.
Invariably stochastically yours,