SAP Business AI implements Intelligent Enterprise processes with SAP S/4HANA Cloud as central core on hybrid multi-cloud Business Technology Platform platforms like SAP BTP or Microsoft Azure.
Business AI Services empower AI use-cases to enhance SAP business processes with predictions, data-driven decisions, recommendations or generated new content. These use-cases are rapidly growing, together with new AI technologies or machine learning model types, offered on cloud platforms like SAP Cloud, Microsoft Azure or Amazon AWS.
Based on the deep knowledge of business processes, SAP develops Large Process Models (LPM) which learn from best practices or recommendations and provide AI powered insights as foundation for Generative Business AI solutions. Beyond that, business process analysis with SAP Signavio offers options to optimize Business AI scenarios driven by data insights.
Cloud architectures integrate AI foundation capabilities like Machine Learning, Knowledge Mining, Natural Language Processing and Computer Vision to implement SAP Business AI solutions and use-cases on multi-cloud platforms. Multi-cloud Business AI solutions can be composed of services from different cloud platforms after comparing service prices and model performance.
With great power comes great responsibility. (Spiderman)
Beside great opportunities and power, measures have to be implemented to manage risks of Business AI and to ensure compliance with legal or ethical principles. Responsible AI shall enhance the transparency and comprehensibility of AI processes and outputs like decisions, recommendations or generated new content.
Responsible AI solutions shall identify potential harms as first step, measure harms in the output, mitigate harms e.g. on safety layers with content filters and define plans to operate Business AI solutions responsibly.
Accountability AI principles define that AI systems are responsible for their actions and humans must have the opportunity to override AI decisions. Transparency of AI systems can be encouraged with documented machine learning model algorithms and transformations, to implement visible, understandable and comprehensable AI scenarios.
AI Fairness principles ensure equal treatment by mitigating bias and discrimination based on gender, race, sexual orientation, or religion. Reliability and Safety AI principles ensure that systems perform consistently as intended in unexpected situations.
Privacy and Security principles provide consumers information and controls to protect their personal data. Inclusive AI systems include all people, empower impaired persons and consider all human races.
Basic machine learning models are trained on historical data with algorithms based on data science and software engineering techniques to find statistical relationships. Trained inferencing functions make predictions based on probabilities with confidence scores.
General purpose models, like pre-trained foundation models, are applicable for a wide range of use-cases, but they can also be fine-tuned with custom data for individual AI scenarios.
Multi-modal models can process different kind of inputs like text or images and convert those prompts into various outputs.
Combining machine learning models with orchestration workflows enables Business AI solutions with advanced capabilities like Optical Character Recognition (OCR) of business documents followed by further NLP processing like Named Entity Recognition (NER) or translations.
These Business AI workflows extract text from documents with positional and layout information to automate business processes like account payable invoice postings.
Natural language processing (NLP) supports capabilities like sentiment analysis, key phrase identification, text summarization, entity recognition, translation or conversational language understanding. These features are based on text analysis and analytics for AI solutions which understand written and spoken words.
Text analysis uses techniques to create 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 of further processes with normalization, stop word removal, stemming or vectorization or embeddings.
Text analytics offers forward orientated insights, trends or patterns with machine learning models in Conversational or Generative 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 the Term frequency - inverse document frequency (TF-IDF) statistical method which measures the token importance within a text corpus or collection of documents.
BLEU (Bilingual Evaluation Understudy) compares automated machine translations and human reference translations to score the quality of translations based on n-gram precision.
MLOps processes can be organized into phases defined in the CRISP-DM (CRoss Industry Standard Process for Data Mining) process model.
Common MLOps phases are business and data understanding, data modeling and deployment of interference models. In the data preparation phase, data loaded via ETL or ELT pipelines can be splitted up into training and validation sub-sets.
Monitoring phases for deployed machine learning models control the degrade of predictive model performance over time and the impact of changes causes by data values, data distribution or business processes.
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.
Advanced compute performance enables processing of machine learning algorithms of Large Language Models (LLM) or Computer Vision with tensors as multidimensional arrays. Hyperparameter control the resource requirements with batch size and number of epochs settings.
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 and backward passes through the complete training 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 is an iterative machine learning optimization algorithm which reduces the cost function of model predictions to improve the model accuracy with optimized weights. Gradient descent variants are batch of all samples in single training set, mini batch and stochastic using one sample per step.
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 one targets 1:1. In contrast to multi-label classifications which assign more than one target to one feature 1:n.
Regression algorithms predict one numerical continuous target with one input variable 1:1 or with multiple input features n:1. Residuals are vertical distances between data points and regression lines with formula r = y − predicted(y).
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 is a supervised learning approach where algorithms select cases for labeling.
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.
Machine learning offers several optimization options like data preparation, feature engineering, error reduction, performance evaluation and continuous improvements.
Feature engineering tunes hyperparameters which control the learning process until the model outcome meets the business goals.
Feature selection tries to reduce independent input, explanatory variables, without losing relevant information.
Some selection methods are Filters with scores as ranking, evaluating subsets with Wrapper functions or Embedded algorithms which can organize unstructured data as feature vectors. Feature scaling normalizes the range of features datasets.
Typical machine learning model errors are Bias, Variance, Overfitting and Underfitting. To reduce such errors, machine learning models have to recognize systematic patterns of deviation (Bias) and avoid to learn variances which are calculated as average of squared differences of the means to avoid model Overfitting with inability to generalize.
Continuous improvements of deployed machine learning can be realized manually or automatically if models own the ability to learn from data and recalibrate autonomously.
Model performance evaluation measures the quality of machine learning models with graphs, indicators or statistics. Confusion matrices to visualize the performance of classification algorithms, Lift charts, profit matrix and ROC curves are examples of visual evaluation methods.
Accuracy explains how often the model predicts correct, Recall shows how often the model can predict the target class, Precision the proportion of true positive of all positive predictions, the F1 Score combines precision and recall.
Additionally, 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.
AI Document Use-Cases
Main intelligent AI Document use-cases are automation of document processing or information extraction for knowledge mining systems. Document Intelligence services 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).
Computer Vision
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 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.
Artificial Intelligence simulates human intelligence in SAP Business AI processes with knowledge from data learned by Machine Learning (ML) models.
Machine Learning is about training models with input parameters and define functions to inference target values with predictions. Machine learning combines data science and software development to enable Business and Generative AI capabilities embedded into intelligent S/4HANA Cloud processes.
SAP S/4HANA Intelligent Scenario Lifecycle Management (ISLM) offers Fiori apps to manage pre-delivered and custom intelligent scenarios embedded based on SAP HANA Machine Learning (ML) Libraries (PAL, APL) and Side-by-Side deployed on the SAP Business Technology Platform (BTP).
Embedded ISLM scenarios are integrated in the ABAP layer with low computing resource requirements and access to SAP HANA Machine Learning Libraries. Typical examples are trend forecasting, classifications or categorizations on structured S/4HANA data.
SAP HANA offers three 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).