Cloud AI Services on SAP BTP or Hyperscaler environments empower SAP transformations to innovative business solutions.
AI Services on Hyperscaler cloud platforms like AWS or Azure are optimized to perform specific tasks in intelligent SAP Business Processes.
These AI Services offer intelligent capabilities in different areas like Computer Vision, Natural Language Processing (NLP) or Generative AI based on specialized or general purpose machine learning models.
Specialized Cloud AI services can be combined to pipelines with multiple steps to process data extraction to model inferencing with AI-powered apps in end-to-end SAP business processes.
Intelligent Document Processing workflows are widely used in various lines of business.
Intelligent Document Processing (IDP) automates data processing of business documents or images and integrates the extracted information into digital business processes. IDP combines processing steps into pipelines which can be implemented with cloud provider specific AI services on different platforms.
Document Information Extraction (DOX) is the Document Intelligence pipeline implementation of SAP on the Business Technology Platform (SAP BTP).
The DOX pipeline forwards the output of OCR document processing, word boxes with text and spatial information, as input sequence to the SAP Charmer transformer model, where neural networks detect the lines in the OCR output, similar to convolutional encoders of the Chargrid algorithm.
Attention layers of the transformer model focus on information of interest and group of similar information to support line item extraction. Annotation functions assign labels to text data of the input document and transformer decoders extract the text from each line for further AI-driven downstream processing with automated workflows or intelligent apps.
Azure offers AI capabilities as multi or single-service resource with RESTful APIs to build applications with AI capabilities. Bundled Azure AI Services require only one key and Url for combined language, vision or search AI solutions.
Azure AI Studios support the complete machine learning lifecycle from data preparation, training, deployment, interferencing and model evaluation with integrated machine learning frameworks such as MLflow. Azure AutoML identifies best algorithms and parameters for specific use-cases automatically.
OCR capabilities are available for documents with the Document Intelligence service ond with Vision AI for images.
Azure AI Search offers cognitive search capabilities as combination of AI with indices which define schemas defined as JSON structures. Knowledge mining creates a searchable knowledge store from huge amounts of structured or unstructured data.
Indexer import data from external data sourcesto create indices as searchable content. Shared private links for are premium features to enable secure outbound calls to Azure PaaS resources.
Document cracking is the first stage in the index creation process which includes the opening of files and extracting content.
Enrichment pipelines integrate built-in skills like OCR, translation or AI Language capabilities into skillsets to provide insights which can be stored in knowledge stores.
Customer-Managed Keys (CMK) require Azure Key Vaults and increase index size with query times.
Increasing replica or partition size can help to resolve performance issues like throttling errors with HTTP errors 503 on service or 207 on index side.
Azure AI Vision offers services with pre-trained models to realize OCR, Image Analysis, Face and Video Analysis AI solutions.
Optical Character Recognition (OCR) offers text recognition or text extraction capabilities for images. Image analysis returns phrases as image description with confidence scores.
The Microsoft Florence foundation model is a pre-trained general model on which you can build multiple adaptive models for specialized tasks like image classification, object detection, captioning or tagging.
Azure AI Custom Vision is an image recognition service that allows you to build and deploy your own image models to predict labels or detect options with supervised machine learning. Models converted to compact domains can be retrained and exported for offline usage.
Safety system layer includes options for content filters to suppress prompts and responses based on security levels.
Azure AI Language offers language understanding and analyzing features to realize cloud AI solutions with Natural Language Processing (NLP) capabilities. Some of these preconfigured, customizable NLP features are Named Entity Recognition (NER), Personally Identifiable Information (PII) detection, Language detection, Summarization, Key Phrase Detection and Question Answering.
Conversational Language Understanding (CLU) enables the implementation of bot capabilities like intent prediction and important information extraction from incoming utterances.
Workflow orchestration models connect bots with Conversational Language Understanding (CLU) or Question & Answering projects.
AutoML automates iterative machine learning development runs with scoring and ranking by specified metrics. Enabling explain best model ensures that the model meets the transparency principle.
Azure AI Services provide two subscription keys to enable regeneration without service interruptions which can be securely stored in Azure Key Vault. These subscription keys can be accessed by clients to initiate token based authentication.
Azure AI services support Microsoft Entra ID authentication with managed identities or service principals, with the difference that managed identities can only be assigned to Azure resources. System-assigned managed identity are coupled to the lifecycle of their linked resource, in contrast to user-assigned managed identity which exist independently of any single resource.
Role Based Access Control (RBAC) enables least privilege security like restricted rotation of subscription keys with contributor roles.
Network access to Azure AI services can be restricted for selected Azure networks, using private endpoints or with Firewall settings for internet or on-premise access. Private Link connections to private endpoints ensure that traffic remains in the Azure backbone.
Service Tags represent groups of IP address prefixes of Azure services to create security rules and routes in network security groups.