SAP AI Core & AI Launchpad Training introduces the tools and practices required to operationalize artificial intelligence and machine learning solutions within SAP environments. Participants learn AI scenario setup, workflow orchestration, model deployment, resource management, monitoring and lifecycle operations. The course also covers integration with SAP BTP services, enterprise data and application landscapes. Through practical exercises, learners develop the skills to manage scalable AI workloads and support reliable, governed AI solutions for real-world business use cases.
INTERMEDIATE LEVEL
1. What is SAP AI Core?
Answer: SAP AI Core is an SAP Business Technology Platform service designed to run and operationalize AI and machine learning workloads. It supports AI scenario execution, workflow orchestration, model training, deployment and lifecycle management in an enterprise environment.
2. What is SAP AI Launchpad?
Answer: SAP AI Launchpad is a centralized user interface for managing AI scenarios and resources across SAP AI services. It helps users monitor deployments, manage AI scenarios and interact with capabilities provided by SAP AI Core and other supported AI services.
3. How are SAP AI Core and AI Launchpad related?
Answer: SAP AI Core provides the runtime and orchestration capabilities for AI workloads while AI Launchpad provides a centralized interface for managing and monitoring those capabilities. In simple terms, AI Core executes AI workloads and AI Launchpad helps users manage them.
4. What is an AI scenario in SAP AI Core?
Answer: An AI scenario represents a defined AI or machine learning use case. It provides an organizational structure for resources such as workflows, configurations, deployments and related AI assets.
5. What is a workflow in SAP AI Core?
Answer: A workflow defines the sequence of processing steps required to execute an AI or machine learning process. It can include activities such as data preparation, model training, evaluation and deployment.
6. What is an execution in SAP AI Core?
Answer: An execution is a runtime instance of a workflow. When a workflow is triggered, SAP AI Core creates an execution that processes the defined steps and produces the corresponding outputs.
7. What is a deployment in SAP AI Core?
Answer: A deployment makes an AI model or service available for inference or other runtime operations. It allows applications to consume an AI capability through an operational endpoint or supported runtime mechanism.
8. How does SAP AI Core support machine learning lifecycle management?
Answer: SAP AI Core supports lifecycle activities such as workflow execution, model training, deployment and monitoring. This helps organizations move AI solutions from development into repeatable operational processes.
9. What is the purpose of AI Launchpad in AI operations?
Answer: AI Launchpad provides centralized visibility and management for AI resources. It allows authorized users to work with AI scenarios, deployments and related services without managing every operation directly through APIs or command-line tools.
10. How can SAP AI Core integrate with SAP BTP?
Answer: SAP AI Core operates as a service within SAP BTP and can work with other BTP capabilities such as identity, connectivity, data and application services. This enables AI solutions to be integrated into broader enterprise architectures.
11. What is model inference?
Answer: Model inference is the process of using a trained machine learning model to generate predictions or outputs from new input data. In SAP AI Core, deployed models can be used as operational AI capabilities.
12. Why is resource management important in SAP AI Core?
Answer: AI workloads can require significant compute and memory resources. Effective resource management helps allocate appropriate capacity, control operational costs and ensure that workloads can run reliably.
13. What role does monitoring play in SAP AI Core?
Answer: Monitoring helps administrators and developers understand the status and behavior of AI workflows and deployments. It can help identify failed executions, performance issues and operational problems.
14. What is the difference between training and deployment?
Answer: Training creates or improves a machine learning model using data. Deployment makes the trained model available for operational use, such as generating predictions for applications or business processes.
15. Why would an organization use SAP AI Core instead of managing AI infrastructure manually?
Answer: SAP AI Core provides managed capabilities for orchestrating and operationalizing AI workloads. This reduces the infrastructure-management burden and helps organizations establish more standardized processes for deploying enterprise AI solutions.
ADVANCED LEVEL
1. How would you design an enterprise AI workflow using SAP AI Core?
Answer: I would first define the AI scenario and identify the required data sources, processing steps and model lifecycle stages. I would then create reusable workflow components for activities such as data preparation, training and inference. Resource requirements, authentication, artifact management, monitoring and deployment strategy should also be considered to make the workflow scalable and maintainable.
2. How does workflow orchestration improve AI operations?
Answer: Workflow orchestration converts individual AI tasks into a repeatable process. Instead of manually executing data preparation, training and deployment steps, dependencies can be defined and executed systematically. This improves consistency, automation, traceability and operational reliability.
3. How would you troubleshoot a failed SAP AI Core execution?
Answer: I would begin by checking the execution status and available logs. Next, I would determine whether the issue is related to workflow configuration, container or runtime dependencies, authentication, resource allocation, input data or an external service. I would isolate the failing component, correct the configuration or implementation and rerun the execution.
4. How can AI workloads be made scalable in SAP AI Core?
Answer: Scalability requires appropriate resource definitions, modular workflows and efficient model-serving architecture. Workloads should be designed so compute resources can be allocated according to processing requirements. Separating training workloads from inference workloads can also improve resource utilization.
5. What is the role of containers in AI Core workloads?
Answer: Containers package application code, libraries and runtime dependencies into consistent execution environments. This helps AI workloads run predictably across environments and simplifies deployment of machine learning components with their required dependencies.
6. How would you manage different versions of a machine learning model?
Answer: I would maintain versioned model artifacts and clearly associate each model version with its corresponding training configuration, data assumptions and deployment. Only validated versions should be promoted to production. Version tracking also makes rollback and performance comparison easier.
7. How can SAP AI Core support CI/CD for machine learning?
Answer: AI workflows and related artifacts can be managed through source-controlled development and automated deployment processes. CI/CD pipelines can validate changes, build required artifacts and promote tested configurations between environments. This provides repeatability and reduces manual deployment errors.
8. What factors should be considered when choosing resources for an AI workload?
Answer: Important factors include workload type, model size, dataset size, CPU or GPU requirements, memory consumption, execution duration and concurrency. Resource selection should balance performance, scalability and cost while avoiding unnecessary allocation.
9. How would you secure an SAP AI Core implementation?
Answer: Security should include strong identity and access management, least-privilege authorization, secure handling of credentials and controlled access to AI resources. Data protection, network security, auditability and separation of development and production environments should also be incorporated into the architecture.
10. How would you integrate enterprise data with an AI workload?
Answer: I would identify the appropriate SAP or external data source and establish secure connectivity. The workflow would then retrieve or process the required data while maintaining appropriate authentication and authorization. Data transformation and validation should occur before the data reaches the model-training or inference stage.
11. What is the importance of observability for production AI workloads?
Answer: Observability provides visibility into execution health, model-serving behavior and system performance. Logs, execution status and relevant operational metrics can help teams identify failures and performance degradation. It is particularly important for detecting issues before they significantly affect business processes.
12. How would you handle a model deployment that is consuming excessive resources?
Answer: I would analyze resource consumption and inference workload characteristics first. The model, serving configuration and allocated resources would then be reviewed. Depending on the findings, I might optimize the model, reduce unnecessary processing, adjust resource allocation or redesign the serving architecture.
13. How can AI Core be incorporated into a broader SAP intelligent enterprise architecture?
Answer: AI Core can serve as the AI execution and operationalization layer while other SAP BTP services provide application integration, data access, identity and connectivity. AI capabilities can then be consumed by business applications and processes, creating an integrated architecture rather than an isolated machine learning environment.
14. What challenges can arise when moving an AI solution from development to production?
Answer: Common challenges include differences in data, runtime dependencies, resource requirements, security policies, model performance and integration interfaces. Production deployments also require monitoring, version control, rollback strategies and appropriate governance. A standardized deployment pipeline helps reduce these risks.
15. How would you evaluate whether an AI solution is production-ready?
Answer: I would evaluate model accuracy and business relevance along with reliability, scalability, security, resource consumption and operational monitoring. The solution should have reproducible deployment processes, appropriate access controls, versioned artifacts and a defined strategy for handling failures, model updates and ongoing performance monitoring.
Course Schedule
| Sep, 2026 | Weekdays | Mon-Fri | Enquire Now |
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| Oct, 2026 | Weekdays | Mon-Fri | Enquire Now |
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