Practical course projects
Every course combines instructor-led learning, guided implementation, project feedback and a final review. These are the integrated projects learners complete.
Plan and execute an evidence-based S/4HANA transformation across readiness, data, custom code, clean-core integration, testing, cutover and operations.
Engineer defensible cloud and AI systems through identity-first architecture, Kubernetes controls, secure delivery, AI threat modelling, detection and incident response.
Design role-based AI agents that coordinate work across enterprise systems through governed tools, APIs, workflow state, permissions and human approvals.
Build reliable batch, streaming and unstructured-data products with contracts, lineage, quality, access controls and observability for analytics and AI workloads.
Design a secure internal delivery platform with Kubernetes, GitOps, golden paths, observability, reliability objectives and technology cost controls.
Corporate learners can work within an approved non-production environment and confidentiality rules. Open cohorts use synthetic data and isolated labs prepared by the teaching team.
We can align a corporate cohort to an approved platform, role profile and learning outcome.