Continuing our blog series after a brief interruption due to the unprecedented Pandemic times, let us now dive into the details of how you can build machine learning and predictive analytics into SAP S/4HANA. In the earlier blog, we discussed briefly the various use cases around embedding and consuming machine learning services with SAP S/4HANA by explaining the way these use cases are organized. Let us now look into the mechanics of how this functionality is embedded into SAP S/4HANA.
In the blog series earlier while discussing the architecture and the different approaches of doing predictive analytics and machine learning with SAP S/4HANA, we discussed the concept of the Predictive Analytics Integrator (PAi) and the key role it plays while embedding this machine learning functionality. Now we are releasing the second version of the PAi which is called Intelligent Scenario Lifecycle Management (ISLM) in Q3 2020. While PAi was focused on embedding HANA’s APL library directly without coding and the HANA PAL algorithms could be embedded using generic coding methodology into SAP S4HANA. Now with ISLM technology, you could embed HANA PAL and HANA APL without any coding into the SAP S/4HANA business applications. The lifecycle management of ISLM handles this approach beautifully. Furthermore, you could also leverage the ISLM technology to leverage the other machine learning algorithms from R programming, Tensor Flow, Python libraries etc., using the side-by-side approach which we shall discuss in the next blog – “Expanding the digital core with SAP Cloud Platform”.
Typically the need is to handle the embedding of machine learning functionality with minimal or no movement of data. These algorithms used in embedded ML are very performance intensive as high volumes of data are handled and processed. As explained earlier with SAP HANA you have the Predictive Analysis Library (PAL) and Automated Predictive Library (APL) application libraries that provide statistical and mining algorithms. While the SAP HANA APL has built-in operations like feature engineering and detection of adequate algorithms, the SAP HANA PAL library provides more than 100 different flavors of ML algorithms. The purpose of PAi aka ISLM is to provide a common interface for the consumption of the ML models independent of the underlying predictive engine to provide predictions and results. This modeling and administrative tool is hugely helpful in managing the complete lifecycle of the model creation, model training, model adoption and model application into the SAP S/4HANA business processes.
The following pictures gives a quick overview of how ISLM can help to articulate using the PAL (also called HEMI – HANA Embedded Machine Language Interface) or APL libraries in creating the models and embed into the SAP S/4HANA applications.
Step 1: Use the Intelligent Scenario Management app
Step 2: Select Embedded modeling
Step 3: Select APL or HEMI (HANA PAL)
In the next blog let us discuss the mechanics behind creating an ML scenario using the side-by-side methodology.
Here are some quick links to the blogs in this series to give you a complete understanding of how Predictive Intelligence is infused into SAP S/4HANA.
- Part 1 – Leveraging Predictive Intelligence with SAP S/4HANA
- Part 2 – Architecture and deep-dive of the different approaches around Predictive Intelligence
- Part 3 – Approaches and the flow leveraging ML and Predictive Analytics for SAP S/4HANA
- Part 4 – Scope and functionality of SAP S/4HANA leveraging ML and Predictive Analytics
- Part 5 – Scope items and how Predictive Intelligence is realized for SAP S/4HANA
- Part 6 – Building into the digital core, SAP S/4HANA (this blog)
- Part 7 – Expanding the digital core with SAP Cloud Platform
- Extending the digital core leveraging SAP Analytics Cloud
- Extensions to the On-Premise and Cloud
We are also releasing a book soon on “Implementing Machine Learning with SAP S/4HANA” by SAP-Press.
Happy predicting the future!!