Choose a capability area to see representative modules, labs, prerequisites and outcomes. Programs are modular rather than rigid: depth and duration can be adapted to the audience and engineering objective.
Build a durable Linux mental model, then progress into administration, networking, security, performance, troubleshooting and production operations.
Connect source control, CI/CD, infrastructure automation, containers, Kubernetes and observability into a coherent engineering delivery system.
Understand distributed data systems and build practical batch, streaming and lakehouse pipelines with production-oriented performance and operability.
Develop programming skill with an engineering focus: data structures, memory, concurrency, networking, APIs, debugging and system-level behavior.
Connect software to hardware behavior through embedded C/C++, Embedded Linux, BSP concepts, drivers, interfaces, debugging and product-level engineering practices.
Understand Android Automotive as a vehicle platform: architecture, services, vehicle integration, HAL/VHAL, security, diagnostics and engineering workflows.
Build security understanding across Linux, networks, applications, DevSecOps, containers, Kubernetes and AI systems with practical engineering controls.
Move beyond AI demos into engineered systems: machine-learning fundamentals, LLM applications, retrieval, agents, evaluation, observability, security and production deployment.
We can combine modules across domains—for example Linux + Kubernetes + Security, Spark + Kafka + Trino, or LLMs + RAG + Agents—based on learner experience, available time and the organization's production environment.