AI and ML
SAP Leonardo Machine Learning is legacy terminology for machine-learning services that included predictive APIs and data-extraction capabilities for content such as images and videos. Current projects should map these references to available SAP Business Technology Platform AI services and verified product documentation.
AI and machine learning continue to influence how organizations analyze information and support business processes. Adoption should be based on a specific use case, representative data, measurable utility, responsible human review, security, integration, monitoring, and accountable ownership—not an outdated market-adoption projection.
Machine-learning foundation
Translate legacy SAP Leonardo terminology into current, verifiable platform services without assuming cost, ease, availability, compatibility, or business outcomes.
Machine-learning services can provide APIs for classification, prediction, extraction, or workflow assistance. Cost, implementation effort, customer or employee experience, automation, and efficiency effects depend on the selected service, data, controls, integration, adoption, and operating model.
Earlier SAP Leonardo materials emphasized cloud deployment, ready-made functions, straightforward algorithm use, and integration with TensorFlow models. Confirm current SAP product names, supported models, capabilities, licensing, regions, security, lifecycle, and support before design.
Potential applications
Machine-learning services can support predictive APIs and data extraction across structured and unstructured content. Whether they improve an operation depends on the problem definition, model behavior, data quality, integration, controls, user workflow, monitoring, and response process.
Image processing, natural-language processing, tabular analysis, and time-series analysis can support selected customer scenarios such as product discovery or personalized shopping. Engagement and satisfaction improvements must be tested and measured.
Cloud-compatible services may connect with approved APIs and web services. Actual integration requires interface design, identity, authorization, privacy, error handling, observability, performance, release controls, and support ownership.
Earlier materials described a pricing model tailored to business needs. Treat pricing, consumption, entitlements, commercial terms, capacity, support, and total cost as current facts to confirm with the applicable provider and contract.
Legacy service topics
Use the service topics below as a requirements checklist, then map them to current product names, available capabilities, data needs, responsible-use controls, integration requirements, delivery competencies, and support ownership.
For current architecture and platform decisions, continue with SAP BTP and evaluate active AI services, responsible-use controls, data readiness, integration, monitoring, and support requirements.
Explore SAP BTPQualify an AI use case