Predictive models
Training, deploying and calling a model that predicts a value from tabular business data: a classification, a regression, a forecast. Distinct from LLM integrationLLM integrationCalling a language model from application code with the tenant, destination and credential handling already solved instead using an SDK call with a hand-rolled key.SAP and community, narrow by design, where a language model generates text, even where both run on the same service.
Overview
SAP AI CoreSAP AI CoreSAP's AI service on BTP: the runtime for AI assets, and through the Generative AI Hub inside it the access to foundation models, the orchestration service with its templating, masking, filtering and grounding modules, the Prompt Registry and Document Grounding.sap official, btp runs training and batch inference as pipelines on Argo Workflows and deploys trained models through serving templates that follow the KServe specification and are kept in a git repository. SAP-RPT-1, deployed on AI Core, returns classification and regression predictions from context rows sent with the request, with no training step.
Not covered: At the container for anything but SAP-RPT. AI Core runs the training code and model server packaged as a Docker image, reading training data from an object store the user supplies, so the model itself and its data preparation are the user's.
SAP's managed services train on the customer's own data: Data Attribute RecommendationData Attribute RecommendationTrains and deploys machine learning models that classify records such as products or users into categories and predict a missing numerical attribute from free text, numbers and categories, over a REST API.sap official, btp classifies records or predicts a missing numerical attribute, and Personalized RecommendationPersonalized RecommendationTrains and deploys a machine learning model over a website visitor's browsing history and item attributes, and serves next-item, similar-item, smart-search and user-affinity recommendations from it over a REST API.sap official, btp serves next-item and similar-item predictions from browsing history. SAP-samples notebooks, workflow templates and workshops demonstrate the AI Core path, and a sample runs the same kind of prediction in SAP Databricks inside SAP Business Data CloudSAP Business Data CloudSAP's umbrella over the data estate: installed intelligent content and data products, a governed catalog they are published through, and a path out to SAP Databricks.sap official, btp; that path is sourced only from its README.
No community artifact is documented here.
Artifacts
Content
Group by10 of 10 pieces
Piece, what it offers
Unlocking Tabular Foundation Models: From Experimentation to ProductionUnlocking Tabular Foundation Models: From Experimentation to ProductionA Devtoberfest 2026 session (AI-41) on taking tabular prediction from a playground to a deployment. It sets tabular foundation models against a model trained per use case and against language models reading rows as text, explains in-context learning as examples plus the rows whose target column is missing, and tries SAP RPT 1.6 on a train-delay dataset in the RPT playground, including the column and row importance it returns with the prediction. The same prediction is then rebuilt on SAP AI Core, where the tabular orchestration service plays the part the orchestration service plays for language models: a data destination, a table artifact with its CSN schema and a scenario configuration registered once at design time, after which a predict call passes only the query rows, the scenario configuration and a context-selection strategy. The last demo runs those same calls in natural language through the aicore-skills plugin in a coding agent, and one of them only resolves once the agent is told which scenario configuration to use.SAP Developers, published 2026-10-15
How We Make Tabular Predictions in 2026 with TabPFNHow We Make Tabular Predictions in 2026 with TabPFNA Devtoberfest 2026 session, AI-32, on why a table needs a model of its own: a language model reasons its way to a plausible number, while a tabular foundation model takes the table as context in one forward pass and learns from its features statistically. That is predictive models from both ends — a scikit-learn-shaped fit and predict in a notebook over a heart-disease data set, predicted class probabilities and SHAP values for which features drove the outcome, then the same model reached as an MCP tool by a coding agent that is asked which current customers are most likely to churn. The presenter is from the lab that publishes TabPFN; the Q&A puts it beside SAP's own tabular foundation model, which she says she has not used.SAP Developers, published 2026-10-08
SAP-RPT-1.5 Is Now Generally Available: More Accurate Predictions, Better Explainability, What's NewSAP-RPT-1.5 Is Now Generally Available: More Accurate Predictions, Better Explainability, What's NewAnnounces general availability of SAP-RPT-1.5, the next release of SAP's foundation model for predictive models over tabular enterprise data: in-context classification and regression with no training step, now with stronger accuracy on high-cardinality columns, a maximum API batch of 512 rows (up from 128), and an "explain by example" mode that surfaces the historical rows that most influenced a prediction rather than ranking feature importance alone. Two variants ship, a default model and a -large model recommended past 100 columns or where accuracy is the priority, and the post previews a coming "tabular orchestration" workflow, targeted for general availability in August 2026, that is meant to select the relevant context rows from millions of historical records automatically.RanjeetKr, published 2026-08-14
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Transforming Enterprise Data Strategy with SAP Business Data CloudTransforming Enterprise Data Strategy with SAP Business Data CloudSAP's layered picture of SAP Business Data Cloud, from lake storage and compute up to a knowledge core and agents, with eight sub-pages that each work one part through. The data products page is the cataloguing and lineage part: datasets described by Open Resource Discovery metadata, listed in the BDC catalog and shared over Delta Sharing to SAP Databricks and SAP HANA Cloud, with SAP Datasphere as a consumer on the intelligent content page; other pages install intelligent content, bring SAP BW data in through the Data Product Generator, provision groups by SCIM from Cloud Identity Services into SAP Databricks, which is identity provisioning, and train predictive models in SAP Databricks that SAP AI Core then serves for batch and real-time scoring. The main page states direction more than mechanism, and the BW page marks SQL Share for derived and OData for analytical data products as to be available.published 2026-05-12
Decision Logic Modernization in SAP Integrations: Using SAP-RPT-1 for Intelligent Routing DecisionsDecision Logic Modernization in SAP Integrations: Using SAP-RPT-1 for Intelligent Routing DecisionsWalks through embedding SAP-RPT-1 — SAP's foundation model for classification and regression over tabular ERP data, hosted on SAP AI Core — into a Cloud Integration flow that picks the optimal 3PL logistics partner for an outbound delivery. The iflow sends historical deliveries from SAP HANA Cloud plus the new delivery as context rows to the model's in-context learning, with no training step, and routes on the returned prediction and confidence score, the mechanism predictive models's core coverage describes for SAP-RPT-1. The author is explicit that SAP-RPT-1 differs from general-purpose language models trained on text, which keeps this apart from an LLM-integration scenario.arunmaarirajha_kv, published 2026-02-16
Federated Machine Learning with SAP DatasphereFederated Machine Learning with SAP DatasphereSAP's design for training predictive models on an external machine learning platform against SAP Datasphere data that is not replicated to that platform. The open-source FedML Python library runs in the platform's notebook, connects to Datasphere over Python/SQLDBC and reads the analytic models in which Datasphere unifies SAP and non-SAP sources; a trained model can then be deployed to SAP AI Core, or in several sub-pages to SAP BTP, Kyma runtime, which is the predictive models path where training happens outside SAP. Sub-pages cover Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Databricks, NVIDIA RAPIDS on GPUs and IBM watsonx.published 2025-01-23
Unlock predictive and generative AI with your data in SAP HANA CloudUnlock predictive and generative AI with your data in SAP HANA CloudThe AI features SAP lists for SAP HANA Cloud: the function libraries for tabular prediction and forecasting, where predictive models run inside the database, the vector engine, the knowledge graph engine, which is graph, spatial and document data put to generative use, and SQL to LLM calls. The abstract names the engines and the use cases they are meant for and gives no detail of any one of them. A TechEd 2026 session (Tuesday 27 October, Berlin; also DA204V, virtual), presented by Christoph Morgen and Javier Martin, written here from SAP's abstract; the recording is not yet published.
Get hands on with tabular AI and forecasting models in SAP HANA CloudGet hands on with tabular AI and forecasting models in SAP HANA CloudPrediction and forecasting as predictive models inside SAP HANA Cloud, worked through hands-on: tabular AI models such as SAP-RPT-1 for on-the-fly predictions, time series foundation models for ad hoc forecasts, and the AutoML in the predictive analysis library. The abstract names the model kinds and the library and does not say how a model is called from SQL or from an application. A TechEd 2026 session (Wednesday 28 October, Berlin), presented by Christoph Morgen, Markus Fath, Ritty Joseph and Tanuja Jadhav, written here from SAP's abstract; the recording is not yet published.
SAP-RPT and TabPFN: Get started with tabular foundation modelsSAP-RPT and TabPFN: Get started with tabular foundation modelsTabular foundation models, SAP-RPT and TabPFN, in a hands-on with SAP and Prior Labs: the model families first, then predictions run through SAP AI Core with a workflow built on tabular orchestration and connected to the participant's own data for real-time predictions. A predictive models session with the models as the subject; the abstract does not name the SDK or API the workflow is built with. A TechEd 2026 hands-on session (Thursday 29 October, Berlin), presented by Eliott Kalfon, Janick Zaehringer-Frasch, Johannes Hoehne, Katharina Schell, Nilay Mehta and Sherene Tan, written here from SAP's abstract; no recording is announced.
Behind the Autonomous Enterprise: SAP Knowledge Graph and SAP Domain ModelsBehind the Autonomous Enterprise: SAP Knowledge Graph and SAP Domain ModelsThe strategy behind SAP Business AI's intelligence layer: SAP Knowledge Graph for enterprise context, SAP Domain Models for SAP-specific expertise and tabular AI for predictions, unified on SAP AI Core with SAP Document AI. Knowledge Graph and Domain Models are not atlas entries, so the edges rest on the two products the abstract names as the infrastructure and on Predictive models for the tabular AI; nothing is stated about what any of them does in the layer. A TechEd 2026 session (Tuesday 27 October, Berlin; also ST103V, virtual), presented by Jan Portisch and Richard Grandpierre, written here from SAP's abstract; the recording is not yet published.
Seen working
ArtifactSignalsLast activity
Samples
ai-core-samplesai-core-samplesSAP-authored tutorial notebooks and workflow templates that walk through a first machine learning project on SAP AI Core, from its object store and Docker images to workflow templates.sap official, btp
ai-core-samples
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aicore-multioutput-regression-restaurant-inspectionsaicore-multioutput-regression-restaurant-inspectionsA boilerplate machine learning service on SAP AI Core that turns restaurant-inspection violation text into several criticality scores.sap official, btp
aicore-multioutput-regression-restaurant-inspections
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analytics-cloud-rpt15-prediction-demoanalytics-cloud-rpt15-prediction-demoA FastAPI backend that triggers SAP-RPT-1.5 payment-delay predictions from an SAP Analytics Cloud Multi Action and writes the results back to HANA.sap official, btp
analytics-cloud-rpt15-prediction-demo
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hana-apl-apis-runtimeshana-apl-apis-runtimesSQL code examples for every SAP HANA Automated Predictive Library API, plus reference runtimes for scoring an exported model in C++, Java or JavaScript.sap official, btp
hana-apl-apis-runtimes
Last pushLast commitLatest release4.2622.0,
hana-ml-sampleshana-ml-samplesSAP-authored code examples for predictive and machine learning scenarios on SAP HANA, from Predictive Analysis Library SQL to hana-ml Python notebooks.sap official, btp
hana-ml-samples
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sap-rpt-samplessap-rpt-samplesBruno API collections and code samples for calling SAP's Relational Pretrained Transformer models on SAP AI Core for predictions on tabular data, one subdirectory per model.sap official, btp
sap-rpt-samples
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SAP-samples/hana-ml-py-codejamSAP-samples/hana-ml-py-codejamSAP CodeJam material that walks through a first machine learning workflow in SAP HANA Cloud from Python, in Jupyter notebooks from HANA DataFrames to hyperparameter tuning and AutoML.sap official, btp
SAP-samples/hana-ml-py-codejam
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teched2023-DA263teched2023-DA263An SAP TechEd 2023 session on SAP HANA features over the HANA Deployment Infrastructure: in-memory column tables, data tiering, spatial, the document store, graph and AutoML.sap official, btp
teched2023-DA263
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teched2025-DA261teched2025-DA261An SAP TechEd 2025 hands-on workshop on SAP HANA Cloud's AI capabilities: outlier analysis, text embeddings with machine learning, and the knowledge graph engine.sap official, btp
teched2025-DA261
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teched2025-DA266teched2025-DA266SAP TechEd 2025 session material for two SAP Databricks scenarios in SAP Business Data Cloud: cashflow forecasting and payment-delay prediction with SHAP explanations.sap official, btp
teched2025-DA266
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