Registering the S/4HANA system as a data source and using a scenario pipeline is the SAP-recommended approach. It leverages the integration capabilities of SAP AI Core, keeps the data flow declarative and manageable within the AI Launchpad, and ensures the model always receives the most current data at inference time without the latency and complexity of direct API calls from the serving code or the staleness of periodic batch exports.

SAP AI Business Services are pre-trained, cloud-based AI services designed for easy consumption in business applications and low-code environments like SAP Build. Services such as Document Information Extraction can be configured to classify document types and extract relevant fields, making them ideal for a low-code priority classification task without the need for custom model training or complex infrastructure management required by AI Core or HANA PAL.

Best practice dictates a staged deployment process. A new model should always be first deployed to a non-productive (e.g., staging or testing) endpoint. This allows for thorough validation, A/B testing, and performance benchmarking against the current productive model without risking business disruption. Direct deployment to a productive endpoint is risky and does not allow for rollback or comparison.

The `instance_type` in a resource plan directly specifies the hardware profile (e.g., number of CPUs/GPUs, amount of memory) of the compute node that will host the container running the training script. This choice is crucial as it directly impacts the speed of model training (especially if GPUs are needed), the ability to handle large datasets in memory, and ultimately the cost of the training execution.

For factual, domain-specific tasks, a RAG pattern is essential. Foundational models like those powering Joule have broad knowledge but lack specific, private company information. RAG works by first retrieving relevant chunks of text from the internal knowledge base (using a search system) and then injecting that context into the prompt sent to the generative model. This allows the model to generate accurate, grounded answers without the cost, risk, and technical challenge of full model retraining.

A Business Entity in SAP Datasphere is a central concept that represents a key business object (like ‘Customer’, ‘Product’, or ‘Sales Order’) with its associated attributes and relationships. By exposing data through these curated, semantic layers, it ensures that data scientists and AI pipelines consume data that is consistent, governed, and aligned with business definitions. This is critical for building reliable and maintainable AI models.

Resource groups in SAP AI Core are the primary mechanism for controlling the location of resources. An administrator can create a resource group that is explicitly tied to a specific cloud region and data center. By assigning your AI scenario (including its configurations, secrets, and workflows) to this region-specific resource group, you ensure that all compute and storage operations (training and inference) are confined to that geographical area, thus enforcing data residency compliance.

A gradual increase in latency with stable volume strongly points to a resource constraint. The initially provisioned compute resources (e.g., CPU/memory defined by the `instance_type`) may have been adequate but are now insufficient, perhaps due to slightly more complex data or internal model operations. This leads to queuing and slower processing. Investigating and scaling up the `instance_type` is a primary remediation step. Other options like an outdated Docker image or schema changes would typically cause failures or sudden latency spikes, not a gradual increase.

SAP AI Core’s pipeline feature is designed specifically for this purpose. It allows you to define a directed acyclic graph (DAG) of distinct, reusable components (each with its own code and environment). The pipeline manages the execution order, passes outputs (like cleansed data or a trained model) from one step as inputs to the next, and tracks the entire run as a single entity. This is more maintainable, reusable, and observable than a monolithic script or manual orchestration.

Registering the S/4HANA system as a data source and using a scenario pipeline is the SAP-recommended approach. It leverages the integration capabilities of SAP AI Core, keeps the data flow declarative and manageable within the AI Launchpad, and ensures the model always receives the most current data at inference time without the latency and complexity of direct API calls from the serving code or the staleness of periodic batch exports.0