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.
SAP S/4HANA business transformations harness AI technologies to implement new AI-powered use-cases on cloud environments. Foundation models are general purpose models, with multi-modal input data options and outputs to generate outputs for a wide range of downstream tasks. Foundation models empower AI-driven intelligent business processes of business solutions like S/4HANA Cloud or offered as SaaS on SAP BTP.
With the deep knowledge of business processes, SAP develops Large Process Models (LPM) which learn from best practices and recommendations. Signavio Process AI integrates these models into business process analysis to optimize Business AI scenarios driven by data insights, recommendations and knowledge repositories.
SAP AI Foundation models are also integrated with Business AI Services to empower AI use-cases for enhanced SAP business processes with predictions, data-driven decisions, recommendations or generated new content.
SAP Business AI strategies implement value proposition with short and mid-term innovations for AI-powered business processes. Modern SAP Business AI combines Machine Learning with MLOps processes to deliver continuous innovations based on tabular Foundation Models (FM) and Knowledge Graphs.
Machine Learning is about training models with input parameters and define functions to inference target values with predictions and confidence scores. Machine Learning (ML) models are trained with data science and software engineering techniques to optimize algorithms with statistical relationships.
Artificial Intelligence simulates human intelligence in SAP Business AI processes with knowledge from data learned by Machine Learning (ML) models. Pipelines combine multiple AI processing steps to realize advanced AI scenarios.
The diagram above visualizes a Intelligent Document Processing pipeline which transforms text or image inputs, with combined machine learning steps, to automate business processes like account payable invoice postings.
SAP Business AI enables data-driven decisions and enterprise automation based on machine learning capabilities like predictions or recommendations. Intelligent Data-to-Value workflows can extend S/4HANA Cloud with SAP BTP data management, analytics and Business AI services.
Multi-modal models can process different kind of inputs like text or images and convert prompts into various outputs. General purpose models, like pre-trained foundation models, are applicable for a wide range of use-cases, but can also be fine-tuned with custom data for individual AI scenarios.
Some Business AI scenarios are delivered out-of-the-box without development requirements, but individual solutions require the implementation of MLOps processes.
The standardized CRISP-DM (CRoss Industry Standard Process for Data Mining) defines process model phases with business and data understanding, data modeling and deployment of interference models.
The accuracy and resource requirements of Machine learning algorithms can be optimzied with Hyperparaneter tuning in the modeling phase.
Gradient Descent hyperparameter tuning is one example, where Epoch and Batch Hyperparameters are used to optimize internal model weights of iterative machine learning optimization algorithms. 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.
Cloud tools and services like SAP BTP AI Core or Azure Machine Learning manage machine learning lifecycles in projects efficiently. MLOps environments handle underlying scalable compute and storage resources to train or serve resource intensive machine learning models and offer development platforms with tools like Jupyter Notebook integrated with popular ML frameworks (e.g. Tensorflow, Scikit-learn).
Data-intensive machine learning workflows can be implemented with tools and frameworks like Metaflow, Argo Workflow or TensorFlow.
Argo Workflows offer custom resources (CRD) on Kubernetes to support direct acyclic graphs (DAG) for parallel processing. Containerized applications can be bundled together with resources as Docker images managed with container registries like Azure ACR or Docker Hub.
Kubernetes is a popular machine learning compute environment with features like service discovery, load balancing within clusters, automated rollouts and rollbacks, self-healing of failing containers and configuration management.
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.
Business AI processes multi-modal input data like natural language, business documents or computer vision.
Natural Language Processing (NLP) harnesses text analysis or analytics and empowers Business AI solutions with capabilities like sentiment analysis, key phrase identification, text summarization, entity recognition, translation or language understanding to predict written or spoken words.
Text analysis creates structured data from unstructured text within collections of documents. Tokenization is one of these techniques to break down a text corpus into tokens as input for further processes with normalization, stop word removal, stemming or vectorization or embeddings.
Text analytics uses forward orientated insights, trends or patterns with machine learning models to realize Conversational or Generative Business AI solutions.
Various techniques and algorithms can be applied to analyze texts, for training or to evaluate processing quality. Simple frequency analysis counts the occurrences of tokens within one document, in contrast to Term Frequency - Inverse Document Frequency (TF-IDF) statistical methods which measure the token importance within a text corpus or collection of documents.
NLP machine learning quality can be measured with methods like BLEU (Bilingual Evaluation Understudy) which compares automated machine translations with human reference translations to score the quality of translations based on n-gram precision.
Automation of document processing or information extraction are steps of intelligent AI Document use-cases to empower AI driven business processes or knowledge mining systems. Document intelligence services can extend basic Optical Character Recognition (OCR) capabilities with pre-trained machine learning models to process common document types like invoices or receipts with PDF, JPEG or PNG formats.
Deep Learning or foundation models enable the extraction of content like text, key-value pairs, selection marks, tables or layout information. Intelligent document services can identity field data in unstructured documents and save this data structured into database tables.
Custom document intelligence solutions have to be trained to return bounding boxes for individual document types.
Example AI document solutions or services are SAP Central Invoice Management, Azure Document Intelligence, SAP BTP Business Entity Recognition or SAP BTP Document Information Extraction (DOX).
Deep Learning algorithms with multi-layered architectures of Convolutional Neural Networks (CNN) enable computer vision services to manipulate and analyse pixels within images. Filters are a common way to perform image processing tasks like highlighting edges of objects with laplace filters.
Object detection and semantic segmentation are two commonly used use-cases of computer vision. Individual locations of items are detected as objects with bounding boxes. Semantic segmentation provides the ability to classify individual pixels in an image depending on the object that they represent.
Knowledge mining enables search capabilities on unstructured data types like text or images by creating search indices. Indexer are crawler which automate data ingestion from data sources like storage, file systems or databases. Enrichment options can be implemented with skill or function sets to extract and enrich data. Knowledge stores persist data generated from AI enrichment.
Cloud environments offers AI services and scalable resources to develop, train, serve and operate machine learning models for Predictive or Generative AI solutions. Tools like Azure AI Studio or SAP AI Launchpad enable the orchestration of containerized compute resources on Kubernetes clusters, the storage of large datasets for training purposes and lifecycle management for training or interference models.
SAP Business AI machine learning capabilities are available as cloud services on the SAP Business Technology Platform (SAP BTP) or embedded into S/4HANA Cloud Core as native SAP HANA libraries. They are optimized to automate business processes with pre-trained machine learning models.
SAP Business AI cloud capabilities can be extended with AI/ML services on Hyperscaler platforms like Microsoft Azure, Amazon AWS and Google Cloud (GCP) platforms.
SAP BTP Business AI Services are ready to run to empower end-to-end business process automation, intelligent business document processing, personal recommendations or central invoice management scenarios.
Cloud platforms offer scalable compute environments like Kubernetes clusters to implement development, traing and inferencing of Machine Learning models. ML instance types can be implemented as Kubernetes custom resource definitions (CRD) with node selection and resource settings.
AWS SageMaker, Azure Machine Learning and SAP AI Core offer Kubernetes clusters as compute targets with operators for defined custom resources (CRD). Kubernetes groups cluster resources with namespaces and orchestrates scalable Pods in AI pipelines.
Argo Workflows can be integrated as container native workflow engine with templates synchronized with Git repositories.
SAP Business AI Machine Learning (ML) solutions offer enterprise ready technologies with lifecycle management to deliver trusted outcomes like recommendations or predictions.
SAP AI Core is a Business AI service on the Business Technology Platform (SAP BTP) with lifecycle management, multi-cloud storage, scalable compute capabilities and multi-tenancy enabled by resource groups. Kubernetes orchestrates the SAP AI workloads on clusters and enables multitenancy with namespaces.
SAP AI Core SDKs integrates Open Source frameworks like Metaflow or Argo Workflow for AI/ML workflow implementations of custom MLOps processes with Git repositories.
Metaflow implements ML flows with steps as execution units and Python decorators to extend functions with additional behaviour. Event based triggering of Argo workflows deployed on Kubernetes clusters can be implemented with Metaflow ArgoEvents which forward ports to Argo Event webhooks.
The SAP Metaflow plugin sap-ai-core-metaflow adds @argo and @kubernetes decorators, command line and environment variables to implement SAP AI Core specific AI ML workflows.
SAP AI Core scenarios are use cases with executable workflows, synchronized between Git repositories and Kubernetes deployments. Scenarios are use cases created with metadata information of workflow templates.
Applications sync automatically with three minutes intervals workflow or serving templates of Git repositories.
SAP Core AI machine learning models are containerized as Docker images and managed in Docker registries. Kubernetes orchestrates these containers on training and serving clusters.
Multi-cloud object stores like AWS S3 or Azure Storage can be integrated to store artifacts like referenced datasets or trained machine learning models.
Argo Workflows implement Custom Resource Definitions (CRD) to orchestrate workflows on Kubernetes clusters. The workflow template definitions are stored in Git repositories and integrated into CI/CD pipelines. KServe templates define the deployment of the Docker images on training and inference servers.
SAP AI Launchpad is a SAP BTP service to manage Business AI scenarios. As part of the AI Launchpad, the Generative AI Hub enables LLM Business AI integrations.
Machine learning frameworks like TensorFlow2 and Detectron2 offer algorithms to detect objects in images with localization and classification capabilities. SAP AI Core supports TensorFlow neural networks with high-level APIs like Keras with optimized user experience and enhanced scaling options.
Furthermore, SAP AI Core SDK Computer Vision package extends Detectron2 to integrate image processing machine learning (ML) scenarios. Image classification quality can be measured e.g. with the Intersection over Union (IoU) to evaluate the inference accuracy.
SAP offers Out-of-the-box Business AI as part of the S/4HANA Cloud solutions.
SAP S/4HANA Intelligent Scenario Lifecycle Management (ISLM) is deeply integrated with S/4HANA to deliver Out-of-the-Box AI with two categories, embedded into the S/4HANA stack and Side-by-Side on the SAP Business Technology Platform (SAP BTP).
S/4HANA ISLM offers two Fiori apps to manage the MLOps lifecycle of intelligent Business AI scenarios from model creation, training to deployment and operation. Predelivered out-of-the-box Business AI scenarios can be used without custom development with ISLM MLOps capabilities.
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. Embedded ISLM scenarios are suitable 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.
Advanced ISLM Business AI scenarios are available Side-by-Side with services on SAP BTP as machine learning provider. Side-by-Side Business AI leverages the power of cloud computing with scalable resources and integrates Out-of-the-Box Business AI based on Document Information Extraction, Data Attribute Recommendation or Generative AI capabilities.
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.
SAP HANA offers three native analysis function libraries (AFL) for data intensive and complex operations.
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).