SAP Business AI implements AI-driven Intelligent Enterprise processes, with SAP S/4HANA Cloud as central core, on hybrid multi-cloud Business Technology Platform platforms like SAP BTP, Microsoft Azure or Amazon AWS.
Cloud Business AI in SAP S/4HANA implements guidelines and best practices to realize relevant, responsible and reliable solutions which transform business with AI-powered use-cases on cloud environments.
SAP Business AI scenarios are realized with different kinds of machine learning models from narrowed scenarios with specialized models to multi-modal foundation models for general purpose Business AI solutions.
The ability of foundation models, to process multi-modal inputs and to generate general purpose outputs, offers the flexibility to implement a wide range of downstream tasks for SAP Business AI scenarios in S/4HANA Cloud or SaaS solutions on SAP BTP.
SAP Business AI solutions can be grounded with the deep knowledge of business processes to optimize Business AI scenarios with data insights, data-driven decisions, recommendations, generated new content or knowledge repositories.
SAP Cloud Business AI strategies implement value proposition with short and mid-term innovations for AI-powered business processes. Short-term Cloud Business AI are available out-of-the-box, deeply integrated into SAP Cloud solutions or can be customized with grounding techniques for specific Business AI use-cases.
Cloud environments like SAP Business Technology Platform (SAP BTP), Azure AI Foundry or AWS SageMaker AI offer ML DevOps and Business AI services to implement advanced solutions with mid-term value proposition to be integrated into SAP Cloud Business AI solutions and processes.
Multi-modal machine learning models offer a wide range of Business AI Areas like Natural Language Processing (NLP), Intelligent Document Processing, Agents, Knowledge Mining or Computer Vision.
Cloud lifecycle management capabilities are prerequisites to integrate AI efficiently into SAP business processes.
MLOps processes integrate Machine Learning with Cloud Business AI services, DevOps CI/CD pipelines and operations to streamline delivery processes of SAP Cloud Business AI solutions. Machine Learning DevOps (MLOps) on Kubernetes clusters implements resource intensive training pipelines and productive deployments of SAP Cloud Business AI solutions.
Kubernetes clusters are scalable environments available on Hyperscaler platforms like Azure Kubernetes (AKS) or AWS Elastic Kubernetes (EKS) services. The containerized environments offer features like internal DNS service discovery, load balancing within clusters, automated rollouts and rollbacks, self-healing of failing containers and configuration management.
SAP AI Core implements SAP Business AI scenarios for business use-cases with MLOps lifecycle management for training and serving workloads on managed Kubernetes clusters.
Multi-tenancy concepts, with SAP AI Core as tenant-aware BTP reuse service, are realized on different levels with resource groups to separate collections of resources mapped to Kubernetes namespaces for tenant specific workloads. Multi-cloud object stores like AWS S3 or Azure Storage store artifacts like referenced datasets or trained machine learning models.
MLOps training steps are defined with Argo Workflow templates and orchestrated with Kubernetes container native workflow engines. Argo Workflow controller manage one pod with three containers for each workflow step or Directed Acyclic Graph (DAG) task.
SAP AI Core implements Argo Workflow templates as executables of training pipelines and KServe as inference platform. Both k8s frameworks extend the Kubernetes API with Custom Resource Definitions (CRD) to control training and serving workloads. SAP AI Core applications use ArgoCD to synchronize Argo Workflow or KServe templates, stored in Git repositories, automatically with Kubernetes.
SAP Business AI training workflow processes are integrated into CI/CD pipelines with Argo Workflow templates created manually or generated from Metaflow pipelines. Argo Workflow behavior can be extended with the SAP AI Core Metaflow Python plugin with decorators for local deployments or event processing.
The SAP AI Launchpad service offers a MLOps lifecycle management environment and the Generative AI Hub to integrate LLMs into SAP Business AI applications.
SAP AI Core offers different options to integrate image processing into business processes with machine learning frameworks or via multi-modal foundation models.
Custom image AI solutions can be implemented with machine learning frameworks like TensorFlow2 or Detectron2 which offer algorithms to detect objects in images with localization and classification capabilities.
The SAP AI Core SDK Computer Vision content package extends Detectron2 with image classification and feature extraction. AI Core Computer Vision implements template driven training pipelines based on metaflow. Computer vision metrics can be visualized using the SAP AI Launchpad, like image classification quality measured e.g. with the Intersection over Union (IoU) to evaluate the inference accuracy.
MLOps extends DevOps processes with machine learning steps to train ML models. Machine Learning (ML) implements data science and software engineering techniques to optimize algorithms of functions as inference targets.
Machine Learning (ML) is a subset of Artificial Intelligence (AI) and learns knowledge from data to simulate human intelligence in Business AI processes. Machine learning functions can be combined as steps in Business AI workflow pipelines to realize advanced AI scenarios.
Intelligent Document Processing, visualized in the diagram above, is one popular example of a Business AI pipeline. Pipelines can combine transformation tasks of text or image inputs with natural language processing (NLP) steps. Business AI workflows can automate document processing of many document types in SAP like purchase orders, sales orders or invoice documents.
Data-to-value workflows can be empowered with Business AI to improve data-driven decisions with insights based on predictions, recommendations or generated content.
Multi-modal data-to-value Business AI workflows can also extend SAP cloud solutions like SAP S/4HANA with enterprise automation capabilities.
Machine learning integrations into SAP ML DevOps processes can be optimized with the standardized CRISP-DM (CRoss Industry Standard Process for Data Mining) process which defines best practices for model phases from business and data understanding, data modeling and to deployments of interference models.
Machine learning models learn functions to predict target values for input features with trained algorithms. These decision functions can be trained on data supervised with labeled targets or unsupervised for unknown labels.
Supervised Training
Classification and regression are supervised training methods with discrete classes or continuous numerical values as targets.
Binary and multi-class classification have 2 or more possible outputs and assign one input to exactly one target 1:1. In contrast to multi-label classifications which assign more than one target to one input feature with cardinality 1:n.
Time series regression methods predict sequences of time data points as targets and enable forecast exploration of future time values outside of known data ranges.
Active learning reduces costs for supervised training and identifies most important cases for labeling manually or with algorithms.
Unsupervised Training
Clustering is a unsupervised training method which groups similar objects together. Because of low memory requirements, K-Means clustering can be used in Big Data scenarios with various data types.
Unsupervised reinforcement training learns with rewards offered by agents as positive feedback for correct actions.
Unsupervised training with Negative Sampling creates negative samples out of true positive instances.
Deep Learning
Deep Learning is a subset of machine learning which simplifies human brain processing with artificial neural networks (algorithms) to solve a variety of supervised and unsupervised machine learning tasks. Deep Learning automates feature engineering and processes non-linear, complex correlations.
Artificial Neural Networks (ANN) are composed of three layers to process tabular data with activation functions and learning weights.
Recurrent Neural Networks (RNN) understand sequential information and are able to process input to output data stepwise.
Convolutional Neural Networks (CNN) are able to extract spatial features in image processing tasks like recognition or classification.
The accuracy and resource requirements of Machine learning algorithms can be optimzied with Hyperparaneter tuning in the modeling phase.
Gradient Descent tuning as example uses Epoch and Batch Hyperparameters to optimize internal model weights of the iterative machine learning optimization algorithm. The model optimization can be measured with a cost function which measures the difference between actual values and predicted values.
Batch sizes define the number of samples, single row of data, which have to be processed before the model is updated. Epochs represent complete forward or backward passes through the complete dataset. Training with smaller batch sizes require less memory, update weights more frequently, with less accurate estimates of the gradient compared to gradients of full batch trainings. Gradient descent variants are batch of all samples in single training set, mini batch and stochastic using one sample per step.
Short-term SAP Cloud Business AI solutions are available within S/4HANA with out-of-the-box intelligent scenarios.
SAP S/4HANA Intelligent Scenario Lifecycle Management (SAP ISLM) offers out-of-the-box AI deeply integrated within S/4HANA Cloud business processes. These intelligent Business AI scenarios are categorized into embedded scenarios, which are implemented with HANA ML libraries within the SAP S/4HANA Cloud stack, and Side-by-Side Business AI scenarios provided by AI services on the SAP Business Technology Platform (SAP BTP).
Advanced SAP S/4HANA ISLM Business AI scenarios have to be realized on cloud environments like SAP BTP, Azure or AWS to leverage the power of cloud computing with scalable resources.
Business AI machine learning providers on SAP BTP are services like SAP AI Core, Document Information Extraction (DOX) or Data Attribute Recommendation (DAR) which are integrated into various out-of-the-box intelligent side-by-side Business AI scenarios.
Embedded intelligent scenarios are implemented within the S/4HANA stack based on native SAP HANA Machine Learning (ML) libraries (PAL, APL) as machine learning provider to implement intelligent Business AI scenarios with low computing resource requirements. Some Business AI examples are forecasting of project costs times, classifications of material groups or regression methods to predict stock movement times.
SAP HANA offers native analysis function libraries (AFL) to implement Business AI within S/4HANA Cloud. Built-in functions and specialized algorithms of the Predictive Analysis Library (PAL) require knowledge of statistical methods and data mining techniques. The Automated Predictive Library (APL) automates most of the steps in applying machine learning algorithms. External machine learning framework for building models like Google TensorFlow can be integrated on-premise with the External Machine Learning Library (EML).
SAP measures the performance of classification or regression models with two indicators, Predictive Power (KI) for accuracy and Prediction Confidence (KR) to indicate the robustness of a predictive model with new data sets.