SAP AI Core — Resource Groups, Scenarios, Executables

What you will learn

• Name and sequence SAP AI Core's six core objects — scenario, executable, configuration, execution, deployment, artifact — and explain how each is built from the one before it
• State precisely which AI Core objects are shared tenant-wide across resource groups and which are scoped to one, and use that boundary to design a resource-group layout
• Write the mandatory labels and annotations that register an Argo WorkflowTemplate as a scenario and executable, including artifact metadata and globalName
• Explain how a Git-registered repository synchronizes into AI Core's scenario and executable catalog, and why reviewing a client's landscape means reading the repository, not just the tenant
• Read a serving template's KServe InferenceService spec and set minReplicas, maxReplicas and containerConcurrency for a realistic traffic pattern
• Apply AI Core's efficiency features — autoscaling, scale-to-zero, scale-to-1, ttl, warm node pool — to balance cost and latency for a given deployment

Module overview

Who this is for. You have built a proof of concept in the generative AI hub, called a model through orchestration, and now a client asks for a real landscape: separate dev, test and production, a place for a data-science team to run training jobs next to the chatbot workloads, and an answer to "who can see what". That landscape is SAP AI Core's object model (resource groups, scenarios, executables, configurations, executions, deployments, artifacts). Most courses skip it because a trial tenant hides it behind one resource group named default. By the end you can design the landscape on paper, create it through the AI API and review a client's tenant for the isolation mistakes listed in the pitfalls chapter. Next module: training and serving custom models.

Prerequisites

• A proof of concept built in the generative AI hub, with a model called through orchestration (“SAP AI Launchpad Operations” for SAP AI Launchpad and the generative AI hub)
• Basic Kubernetes and YAML vocabulary (namespace, secret, container image, replica)
• For the hands-on part: an SAP AI Core service instance and service key, the extended service plan for generative AI, and a bearer token from the service key

Outcomes

• Name SAP AI Core's six objects in dependency order (scenario, executable, configuration, execution or deployment, artifact) with SAP's definitions.
• State which objects are scoped to a resource group and which are shared across the tenant, and explain why a shared bucket or IAM credential breaks isolation.
• Write the labels and annotations that register a scenario and executable from an Argo workflow template, including globalName on output artifacts.
• Create a resource group, onboard a repository, create a configuration and a deployment through the AI API, and confirm the deployment is refused for another resource group.
• Choose a scaling policy per deployment (scale-to-zero, scale-to-1, warm nodes, ttl) and test a landscape with four checks per resource group.