SAP AI Core Training Online Certification Course
Advance your enterprise AI capabilities with SAP AI Core Training by Multisoft Systems. This comprehensive program introduces learners to SAP’s AI runtime environment for managing machine learning and generative AI workloads at enterprise scale. The training covers SAP AI Core architecture, resource groups, AI scenarios, Docker-based workloads, Git repositories, model training, deployments, inference, APIs, SAP BTP connectivity, Generative AI Hub, monitoring, security, and operational practices. Practical exercises provide exposure to configuring and running AI workloads within SAP-oriented business environments.
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SAP AI Core Training Online Certification Course Course Overview
SAP AI Core Training by Multisoft Systems provides structured learning on using SAP AI Core to execute, manage, and operate artificial intelligence and machine learning workloads within enterprise environments. The course begins with the role of SAP AI Core in the SAP Business AI landscape and progresses through its architecture, configurations, resource groups, applications, Git-based workflows, Docker containers, and workload orchestration.
Participants learn how AI scenarios are configured, how training executions are initiated and monitored, and how trained models can be deployed as scalable inference services. The curriculum also addresses API-based interaction, SAP BTP connectivity, credentials, secrets, resource management, monitoring, logs, troubleshooting, and security considerations.
The course further introduces Generative AI Hub concepts and explains how generative AI capabilities can be consumed within SAP-centric solutions. Hands-on exercises and end-to-end scenarios help participants understand the operational lifecycle of enterprise AI workloads from initial configuration and model training to deployment, inference, monitoring, and ongoing management
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SAP AI Core Training Online Certification Course Course curriculum
Curriculum Designed by Experts
SAP AI Core Training by Multisoft Systems provides structured learning on using SAP AI Core to execute, manage, and operate artificial intelligence and machine learning workloads within enterprise environments. The course begins with the role of SAP AI Core in the SAP Business AI landscape and progresses through its architecture, configurations, resource groups, applications, Git-based workflows, Docker containers, and workload orchestration.
Participants learn how AI scenarios are configured, how training executions are initiated and monitored, and how trained models can be deployed as scalable inference services. The curriculum also addresses API-based interaction, SAP BTP connectivity, credentials, secrets, resource management, monitoring, logs, troubleshooting, and security considerations.
The course further introduces Generative AI Hub concepts and explains how generative AI capabilities can be consumed within SAP-centric solutions. Hands-on exercises and end-to-end scenarios help participants understand the operational lifecycle of enterprise AI workloads from initial configuration and model training to deployment, inference, monitoring, and ongoing management
- Understand the purpose and capabilities of SAP AI Core.
- Explain the role of SAP AI Core within SAP's enterprise AI landscape.
- Identify major SAP AI Core architectural components and services.
- Understand tenants, service instances, resource groups, and configurations.
- Prepare environments required for running AI workloads.
- Work with Git repositories and application configurations.
- Understand containerized AI workloads using Docker images.
- Configure AI scenarios, executables, artifacts, and workflows.
- Initiate and monitor machine learning training executions.
- Manage model artifacts generated through training processes.
- Configure model serving and inference deployments.
- Interact with SAP AI Core through APIs and related tools.
- Understand connectivity between SAP AI Core and SAP BTP applications.
- Explore Generative AI Hub and generative AI consumption patterns.
- Apply appropriate security, credentials, and secret-management practices.
- Monitor executions and deployments using logs and status information.
- Diagnose common workload and deployment issues.
- Understand resource allocation and lifecycle management.
- Apply SAP AI Core concepts through practical enterprise scenarios.
Course Prerequisite
- Basic understanding of artificial intelligence and machine learning concepts.
- Familiarity with cloud computing fundamentals is beneficial.
- Basic knowledge of SAP BTP can help participants understand integration scenarios.
- Familiarity with Git and source-code repositories is useful.
- Basic understanding of Docker and container concepts is recommended.
- Knowledge of Python, APIs, or machine learning workflows can be advantageous for hands-on exercises.
- Prior SAP AI Core experience is not mandatory.
Course Target Audience
- SAP AI and Machine Learning Professionals
- SAP BTP Consultants
- SAP BTP Developers
- AI/ML Engineers
- Data Scientists
- Machine Learning Engineers
- Generative AI Professionals
- SAP Technical Consultants
- SAP Solution Architects
- Cloud Engineers
- MLOps Professionals
- Application Developers working with SAP environments
- Enterprise Integration Professionals
- SAP Business AI Consultants
- Technical Leads involved in AI initiatives
- Professionals working on SAP-based AI transformation projects
- IT professionals seeking knowledge of enterprise AI workload management
Course Content
- Introduction to enterprise artificial intelligence
- AI and machine learning in SAP environments
- Overview of SAP Business AI
- Introduction to SAP AI Core
- Purpose and capabilities of SAP AI Core
- SAP AI Core as an AI runtime
- Understanding AI workload orchestration
- Relationship between AI Core and AI applications
- SAP AI Core and SAP BTP landscape
- Overview of the AI workload lifecycle
- Typical enterprise use cases for SAP AI Core
- Understanding training and inference workloads
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- Understanding SAP AI Core architecture
- Core services and architectural elements
- SAP AI Core tenants
- Service instances and service keys
- Resource groups and workload isolation
- AI scenarios and configurations
- Executables and templates
- Executions and deployments
- Artifacts and models
- Applications and repositories
- Understanding configurations and parameters
- Connections between major SAP AI Core objects
- AI workload lifecycle within the architecture
- Understanding runtime resources
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- SAP AI Core environment requirements
- SAP BTP account and subaccount considerations
- Creating and configuring SAP AI Core service instances
- Understanding service plans
- Creating service keys
- Accessing SAP AI Core services
- Authentication fundamentals
- Working with SAP AI Core APIs
- Introduction to command-line and API interaction
- Resource group creation and management
- Configuring credentials
- Working with secrets
- Understanding object storage requirements
- Preparing repositories for AI workloads
- Validating initial environment configuration
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- Understanding containerized AI workloads
- Introduction to Docker concepts for SAP AI Core
- Preparing AI applications for container execution
- Docker images and container registries
- Image references and version management
- Understanding executables
- Executable templates and specifications
- Parameters and placeholders
- Input and output artifacts
- AI scenario association
- Resource requirements for workloads
- Environment variables
- Secrets within workload configurations
- Container execution lifecycle
- Best practices for organizing AI workloads
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- Role of Git in SAP AI Core
- Repository structure for AI applications
- Connecting Git repositories
- Repository credentials and authentication
- Application configuration
- Understanding application synchronization
- Workflow definition concepts
- YAML-based configuration fundamentals
- Organizing scenarios and executables
- Managing configuration changes
- Version-controlled AI workload definitions
- Updating application resources
- Validating synchronization status
- Troubleshooting repository and synchronization issues
- Configuration management practices
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- Understanding machine learning training workloads
- Preparing training code for SAP AI Core
- Training executable configuration
- Creating training configurations
- Working with input datasets
- Artifact management
- Parameters for training executions
- Starting training executions
- Monitoring execution status
- Understanding execution states
- Accessing workload logs
- Tracking training progress
- Handling failed executions
- Output artifact generation
- Model artifact management
- Re-running training with modified configurations
- Resource considerations for model training
- Training workflow troubleshooting
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- Introduction to model serving
- Understanding inference workloads
- Preparing models for deployment
- Serving executable configuration
- Creating deployment configurations
- Creating and managing deployments
- Deployment lifecycle and states
- Accessing deployment endpoints
- Sending inference requests
- Understanding request and response structures
- Authentication for inference endpoints
- Model version considerations
- Updating deployed models
- Monitoring deployment health
- Scaling and resource considerations
- Stopping and deleting deployments
- Troubleshooting inference failures
- Model serving operational practices
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- Introduction to generative AI in SAP environments
- Role of Generative AI Hub
- Generative AI Hub within SAP AI Core
- Understanding foundation model access
- Model selection considerations
- Generative AI use cases in enterprise applications
- Working with prompts
- Prompt parameters and model responses
- Understanding inference for generative AI
- Connecting applications to generative AI capabilities
- Generative AI API interaction concepts
- Managing model access
- Responsible use considerations
- Data privacy considerations for generative AI
- Evaluating generative AI responses
- Enterprise scenarios using generative AI
- Relationship with SAP Business AI applications
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- Overview of SAP AI Core APIs
- API authentication and authorization
- Working with service credentials
- Managing resource groups through APIs
- Accessing scenarios and configurations
- Managing executions programmatically
- Managing deployments through APIs
- Retrieving execution and deployment status
- Accessing logs and operational information
- Integrating SAP AI Core with SAP BTP applications
- AI service consumption patterns
- Application-to-AI Core communication
- Handling inference responses
- Integration considerations for enterprise applications
- API error handling
- Connectivity troubleshooting
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Monitoring and Operations
- Monitoring AI executions
- Monitoring deployments
- Understanding workload states
- Accessing and interpreting logs
- Tracking failed and completed workloads
- Deployment health monitoring
- Operational visibility for AI workloads
Security and Access
- Authentication concepts
- Authorization considerations
- Service credentials
- Resource-group-based isolation
- Managing secrets securely
- Repository credentials
- Container registry credentials
- Protecting AI endpoints
- Enterprise security considerations
Resource Management
- Understanding compute resource requirements
- CPU and memory considerations
- Resource allocation for training
- Resource allocation for serving
- Managing concurrent workloads
- Workload efficiency considerations
- Cleaning unused executions and deployments
Troubleshooting
- Diagnosing configuration issues
- Git synchronization failures
- Container image errors
- Training execution failures
- Deployment initialization issues
- Inference endpoint errors
- Credential and authentication problems
- Resource-related failures
- Log-based troubleshooting techniques
- Common configuration mistakes
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- Preparing an SAP AI Core project structure
- Connecting an AI workload repository
- Configuring a resource group
- Creating an AI scenario
- Preparing executable definitions
- Configuring training parameters
- Running a model training execution
- Monitoring training logs and execution status
- Managing generated model artifacts
- Preparing a serving executable
- Creating an inference deployment
- Testing the deployed endpoint
- Updating deployment configurations
- Exploring a generative AI consumption scenario
- Connecting an application to AI capabilities
- Applying credentials and secrets
- Monitoring the complete workload lifecycle
- Diagnosing common implementation issues
- End-to-end enterprise AI workflow exercise
- Operational and deployment best practices
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SAP AI Core Training (MCQ) Assessment
This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :
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SAP AI Core Training Online Certification Course Trainer Profile
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Our SAP AI Core Training Corporate & Certification Program trainers bring 13+ years of proven industry expertise, delivering practical insights aligned with real project environments.
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Our expert trainers have successfully trained 3350+ professionals through structured, real-time training programs designed for industry readiness and career growth.
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Build strong practical skills through live project-based training sessions led by certified industry experts with real-world experience.
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Gain practical exposure through real-time scenarios, industry case studies, and hands-on assignments that simulate actual project challenges.
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SAP AI Core Training Online Certification Course FAQ's
SAP AI Core Training focuses on the concepts and practical processes involved in running, managing, deploying, and monitoring AI and machine learning workloads using SAP AI Core in enterprise environments.
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