Hands-On Workshops for Embedded Edge AI Systems
From Data to Intelligence at the Edge
To help developers build data-sensitive, AI-enabled embedded products, ITTIA delivers interactive Edge AI workshops that combine deterministic data management, real-time processing, and on-device intelligence. These hands-on sessions ensure participants can efficiently select and integrate the right hardware and software stack to meet the unique requirements of Edge AI systems.
The workshops cover best practices and advanced techniques for data ingestion, time-series storage, preprocessing, feature extraction, and AI-ready pipelines on embedded targets. Participants work with production-grade software such as ITTIA DB Lite, ITTIA DB, Edge AI frameworks (e.g., STM32Cube.AI, NanoEdge AI Studio, NXP eIQ, TensorFlow Lite Micro, etc.), and secure connectivity and visualization tools. Hardware platforms include MCUs, MPUs, NPUs and ECUs from leading vendors, such as STMicroelectronics, NXP, and other edge-class processors, paired with real sensors (vibration, current, temperature, vision, and more).
Real-World Edge AI Experience
Through real-world Edge AI scenarios, predictive maintenance, anomaly detection, condition monitoring, and intelligent control, developers gain practical experience that directly translates into higher productivity, lower integration risk, and faster time to deployment. Each workshop emphasizes deterministic behavior, power-fail safety, explainability, and long-term data reuse, critical requirements for industrial, automotive, medical, aerospace, and energy systems.
Edge AI Framework Foundations
The workshop includes a dedicated Edge AI framework introduction training that gives developers a clear, practical foundation for building intelligent, on-device systems. This training introduces leading Edge AI frameworks and toolchains and explains how they fit into real embedded workflows, from data acquisition and preprocessing to model deployment and inference on embedded devices. Participants learn the strengths and tradeoffs of different frameworks, how to map AI workloads to resource-constrained hardware, and how to integrate AI inference with real-time firmware, deterministic data management, and power- and safety-critical requirements. Through hands-on, production-oriented examples, the workshop enables developers to move beyond experimentation and confidently adopt Edge AI frameworks as part of a robust, data-centric embedded architecture.
Building a Complete, Data-Centric Edge AI Architectural Core
By attending these sessions, developers unlock the full potential of data-centric Edge AI, transforming raw device data into actionable intelligence with a clear operational strategy. Our tailored, application-driven approach builds confidence while equipping you with the hardware platforms, software components, and architectural patterns needed to solve complex data challenges at the edge.
Participants leave with a complete, data-centric Edge AI foundation, including reference architectures, design patterns, and hands-on experience, that enables them to build intelligent embedded systems that learn, adapt, and improve throughout their lifecycle. These workshops are a smart investment to maximize the value of your Edge AI software stack, accelerate innovation, and deepen your collaboration with ITTIA.
Data-Centric Edge AI Workshop Formats
ITTIA offers two complementary data-centric Edge AI workshop formats designed to help developers turn device data into reliable, on-device intelligence.
- On-site, laboratory-based workshops provide deep, hands-on exploration of Edge AI data architectures, covering hardware platforms, sensors, and software stacks while emphasizing deterministic data ingestion, processing, storage, and AI integration.
- Online, tutorial-based workshops are tightly focused on a single, end-to-end data-centric Edge AI demonstration, showing how raw device data flows through preprocessing, analytics, and AI inference to produce actionable results.
Together, these workshop formats give teams flexible learning paths, either deep technical immersion or fast, outcome-driven execution, while keeping data management, explainability, and production readiness at the center of every Edge AI solution.
