The website content has been organized around the terms that engineering teams commonly search for, including Embedded Systems Training, RTOS Training, Embedded Linux Training, Linux Device Driver Training, Automotive Software Training, Android Internals Training, C++ Training, AI Training and IoT Training.
Embedded Systems Training
Training for engineers working on microcontroller-based and microprocessor-based products. Topics include embedded architecture, memory-mapped I/O, interrupts, timers, boot flow, linker scripts, startup code, peripheral programming, firmware design, debugging and embedded product development practices.
- Embedded C and bare-metal firmware
- ARM Cortex-M and Cortex-A concepts
- Board bring-up, bootloaders and firmware update flow
- UART, SPI, I2C, GPIO, ADC, PWM and timer programming
RTOS Training and FreeRTOS Training
Real-Time Operating System training for teams developing deterministic embedded applications. The course can cover task scheduling, preemption, priorities, context switching, synchronization, inter-task communication, timing analysis and real-time debugging.
- FreeRTOS tasks, queues, semaphores, mutexes and event groups
- Priority inversion, deadlocks, latency and jitter analysis
- Timers, stream buffers, task notifications and ISR interaction
- RTOS design patterns for reliable embedded products
Embedded Linux Training
Embedded Linux training for product engineering teams using Linux on ARM platforms and custom boards. The course can include Linux architecture, kernel configuration, device tree, bootloaders, root file system creation, system services and target debugging.
- U-Boot, Linux kernel, device tree and root file system
- Yocto Project, Buildroot and custom image generation
- Systemd services, logging, networking and deployment
- Cross-compilation, remote debugging and performance tools
Linux Device Driver Training
Device driver training for engineers who need to interface hardware with the Linux kernel. Training can cover character drivers, platform drivers, kernel modules, device tree bindings, interrupt handling, workqueues, kernel synchronization and debugging.
- Character drivers and platform drivers
- Device tree nodes, resources and probe/remove flow
- Interrupts, wait queues, workqueues and kernel timers
- Kernel logs, ftrace, perf, crash analysis and driver debugging
Automotive Software Training
Training for automotive embedded software teams working on connected, software-defined and safety-conscious vehicle platforms. The course can include AUTOSAR concepts, automotive communication protocols, diagnostics, update flows and gateway design.
- Classic AUTOSAR and Adaptive AUTOSAR overview
- CAN, CAN FD, LIN, FlexRay and Automotive Ethernet
- UDS, DoIP, SOME/IP, service discovery and diagnostics
- FOTA, gateway architecture and cybersecurity basics
Android Internals and Automotive Android Training
Android platform training for engineers working on embedded Android, infotainment systems and platform customization. Training can cover Android architecture, native services, Binder IPC, HAL, AOSP build flow, SELinux and Automotive Android concepts.
- AOSP architecture, framework, native layer and HAL
- Binder IPC, services, permissions and system startup
- SELinux policy basics and Android security architecture
- Automotive Android, vehicle HAL and platform integration
C, C++ and Modern C++ Training
Programming training for embedded, automotive and systems software teams. Courses can be customized for Embedded C, C++14, C++17, C++20, object-oriented design, design patterns, memory management, concurrency and performance-aware software development.
- Embedded C, pointers, memory layout and defensive coding
- C++ object model, RAII, smart pointers and move semantics
- STL, templates, concepts, concurrency and coroutines
- Design patterns for embedded and automotive software
AI, Machine Learning and Data Science Training
AI and Machine Learning training for organizations that want practical exposure to data science, Python, machine learning workflows, model evaluation, neural networks, generative AI tools and engineering use cases.
- Python for data analysis, NumPy, pandas and visualization
- Supervised learning, classification, regression and clustering
- Deep learning basics, model evaluation and deployment concepts
- Generative AI use cases, prompt workflows and responsible adoption
