Machine Learning Inference
Intelligent Scenario Lifecycle Management (ISLM) is an SAP BTP capability that enables developers and administrators to manage the full lifecycle of intelligent scenarios — including machine learning models and AI-based predictions — within SAP applications. According to the official documentation, it provides tooling to govern how intelligent scenarios are deployed, activated, and maintained. Developers use it to operationalize AI scenarios such as sales order completion, service ticket classification, and recommendation models within the SAP ecosystem.
Tutorials that teach this
- Tutorial Use Service Ticket Intelligence and Jupyter Notebook to Classify Service Requests
- Tutorial Use the Sales Order Completion (SOC) Business Blueprint to Predict Missing Sales Order Fields
- Tutorial Use Your Own Data to Train a Machine Learning Model and Get Recommendations Based on Users' Browsing History
- Tutorial Use Data Attribute Recommendation With Your Own Dataset
- Tutorial Use the Python SDK for Business Entity Recognition
- Tutorial Deploy Model and Get Prediction Results
- Tutorial Consume the SDK for Data Attribute Recommendation using the AI API from SAP AI Core
- Tutorial Use Service Ticket Intelligence and Jupyter Notebook to Get Solution Recommendations
- Tutorial Use Service Ticket Intelligence and Postman to Classify Service Requests
- Tutorial Make Predictions for House Prices with SAP AI Core
- Tutorial Use the Regression Model Template to Predict Data Records
- Tutorial Use Service Ticket Intelligence and Postman to Get Solution Recommendations
- Tutorial Get Recommendations Based on Users' Browsing History
Docs explaining this concept
Prerequisites
- Concept Custom Dataset Schema Definition for ML **Data Attribute Recommendation** is an SAP AI service that developers use to predict missing or incorrect field values in business data records. It supports multiple use cases, including predicting missing sales order fields via the [Sales Order Completion Business Blueprint](https://developers.sap.com/tutorials/cp-aibus-dar-sdk-soc.html), recommending financial objects via the Invoice Object Recommendation Business Blueprint, classifying records, and performing regression-based predictions. Developers can work with built-in business blueprints, custom dataset schemas, or their own datasets, and can consume the service through the AI API from SAP AI Core.
- Concept SAP Data Attribute Recommendation Service The provided sources do not define a specific SAP developer concept — the concept submitted is "undefined." The available sources cover tutorials related to **Data Attribute Recommendation**, an SAP AI service that developers use to classify records, train machine learning models, manage datasets, and obtain OAuth access tokens for authentication. If you intended to define a specific concept (such as "Data Attribute Recommendation" or one of its related topics), please resubmit with a valid concept name.
- Concept OAuth 2.0 Client Credentials Retrieval The provided sources do not contain sufficient information to define the concept "undefined." The sources cover topics such as Communication Arrangements, audit log access, credential retrieval, and API configuration on SAP BTP, but none of them define or describe a concept explicitly named "undefined." A reliable definition cannot be grounded solely in the provided snippets.
- Concept Swagger UI API Interaction The concept name was not provided and cannot be determined from the available source snippets, which cover a range of SAP machine learning and data recommendation tutorials. Without a defined concept to describe, a grounded definition cannot be written. Please provide a valid concept name to generate an accurate reference definition.
- Concept OAuth Access Token Retrieval The concept provided is **undefined** — no concept name was supplied for definition. Based on the available sources, a definition cannot be accurately scoped or written without knowing the specific concept to define. If you intended to ask about a concept such as **mTLS (Mutual Transport Layer Security)** for access tokens, or **OAuth Access Tokens** on SAP BTP, please resubmit with a clearly named concept so it can be grounded in the provided sources.
- Concept Predictive Modeling and Model Training The concept provided is **undefined** — no concept name was supplied for this entry. Please provide a valid SAP developer concept so that an accurate, source-grounded definition can be written.
- Concept ML Model Deployment and Activation The provided sources do not contain enough information to write a grounded definition for an "undefined" concept. Source S1 references configuring communication systems and arrangements in the context of [managing Gen AI scenarios](https://help.sap.com/docs/btp/sap-business-technology-platform/5fef7203b3294400988c2b1f4bd462a6?locale=en-US&state=PRODUCTION&version=Cloud), while the remaining sources describe machine learning tutorials, but no specific named concept is identified or defined across these snippets.
- Concept Machine Learning Model Training The provided sources do not contain enough information to write a grounded definition for an undefined concept. No concept name or supporting source content was supplied.
- Concept Machine Learning Model Deployment The concept name provided is "undefined," so no specific SAP developer concept can be accurately defined. Based on the available sources, the closest supported topic relates to deploying and undeploying machine learning models on SAP Business Technology Platform: once training has successfully completed for a model, it can be [deployed](https://help.sap.com/docs/btp/sap-business-technology-platform/d85117a56b094b309c56d99afa6bcab2?locale=en-US&state=PRODUCTION&version=Cloud) so that it is available for generating predictions. Developers can also manage the full lifecycle of such models using [Intelligent Scenario Lifecycle Management](https://help.sap.com/docs/btp/sap-business-technology-platform/2217809cdb5842bf98211c61a8ef55ca?locale=en-US&state=PRODUCTION&version=Cloud), which provides tooling to oversee intelligent scenarios from training through deployment.
Concepts that build on this
- Concept Core ML Model Integration in Xcode
- Concept Async Job Polling Pattern (POST/GET background job)
- Concept SAP Data Attribute Recommendation Service
- Concept Service Ticket Intelligence
- Concept SAP Personalized Recommendation Service
- Concept LLM Batch Inference (Asynchronous)
- Concept Asynchronous Job Status Tracking
- Concept Named Entity Recognition (NER) with Pre-trained Models
- Concept Document AI invoice processing
- Concept Machine Learning Model Undeployment