Building Data-Centric Edge AI for Weather Station Devices
Online Training Workshop: Build, Learn, Deploy
Overview
This workshop is designed to introduce developers to the hardware and software required to build data-centric Edge AI applications for weather station monitoring and predictive maintenance. Participants learn how meteorological data, such as wind intensity, wind direction, temperature, and barometric pressure is acquired, processed, and managed deterministically at the device, and how this data is transformed into AI-ready features for on-device inference. The workshop covers the selection and integration of edge hardware (MCUs, MPUs, NPUs, sensors, and accelerators) alongside production-grade software for real-time control, time-series data management, signal processing, and Edge AI frameworks. Through hands-on exercises, developers gain a clear understanding of how data management, analytics, and AI work together to detect anomalies, predict failures, and enable reliable, explainable motor health intelligence directly at the edge.
Workshop Date: Thursday, April 30, 2026
Workshop Time: 11:00 AM–2:00 PM PT (2:00 PM–5:00PM ET)
Workshop Location: Online
Online Tutorial Workshop Syllabus
Objective: Detect abnormal environmental conditions in real time on an Edge device. Build data-driven firmware to ingest raw weather signals, generate cleaned features, analyze environmental patterns with Edge AI, and visualize a timeline of system and environmental conditions.
The workshop comprises three 60-minute modules.
Module 1: Data-Driven Firmware Design and Programming
- Sensors for environmental monitoring
- Sample weather signals such as wind speed, wind direction, temperature, humidity, and barometric pressure
- Hardware setup
- Embedded device, storage media, and communication interfaces for weather station deployment
- Data modeling for edge devices: environmental monitoring & anomaly detection
- Ingest and store sensor samples, features, inference results, events, and system metrics
- Define sampling rates, stream window sizes, and key data structures
- Data cleaning and conditioning with deterministic stream buffering
- Per-sensor filtering, normalization, outlier detection, and handling of missing data
- Transactional commits and time-series storage for reliable data management
- Feature engineering for Edge AI
- Extract features such as mean, variance, peak values, trends, and environmental pattern indicators for real-time analysis
Module 2: Building an Edge AI Pipeline
- Select AI model algorithm
- Anomaly detection vs. classification for environmental patterns
- Environmental datasets with variations/disturbances
- Sudden temperature changes, wind gusts, pressure drops, humidity spikes, and sensor noise
- Train a model with an Edge AI development tool
- Train using normal environmental conditions, validate with varying and abnormal scenarios
- Assess model size, latency, and deterministic performance
- Export the optimized model to the embedded runtime
- Real-time inference and environmental analytics pipeline
- Integrate ML model inference into feature streams to generate anomaly scores or condition classifications per time window
- Define thresholds, persistence logic, and environmental state models (e.g., normal, alert, extreme conditions)
- Inject simulated environmental disturbances and observe model response and anomaly score transitions
Module 3: Edge Device Analytics, Visualization, and Connectivity
- Communicate with ITTIA Data Connect
- Over Ethernet, WiFi, and/or serial UART
- Create a custom on-device summary dashboard with ITTIA Analitica
- Display metrics such as average temperature, peak wind speed, humidity trends, pressure changes, and number of anomalies
- Visualize data through web dashboards, web services (HTTP/REST), Python, and C/C++ interfaces
- Analyze environmental patterns
- Frequency, duration, and severity of abnormal weather conditions
- Identify trends such as rapid temperature shifts, pressure drops, or wind variability
- Correlate multi-sensor data
- Relate wind patterns with pressure changes
- Analyze temperature and humidity interactions
- Detect combined environmental effects
- Score environmental conditions
- Evaluate anomaly counts, severity trends, and extreme condition indicators
- Generate environmental state or alert levels
- Use anomaly data for diagnostics and insight generation
- Identify unusual weather events and system behavior
- Support forecasting and environmental analysis
- Validate system performance
- Ensure bounded latency per processing window
- Maintain efficient storage usage and long-term reliability
Workshop Sample Application
During this hands-on workshop, you will be trained to design, build, and deploy a real-world, data-driven AI application, gaining practical skills you can immediately apply to embedded and edge systems.
Anomaly Detection in a Weather Monitoring System
During the workshop, participants take on the role of an embedded engineer responsible for ensuring reliable and intelligent operation of a weather monitoring device in real-world environments. Using MCU-based hardware, they begin by capturing live environmental signals, such as wind speed, temperature, humidity, and barometric pressure, directly at the edge, without reliance on cloud connectivity. The team trains an on-device AI model that learns what “normal” environmental patterns look like and continuously adapts to changing conditions.
As the system operates, subtle anomalies begin to emerge, sudden wind gusts, unexpected temperature shifts, pressure drops, or sensor noise, early indicators that may not be detected by traditional threshold-based approaches. The AI detects these anomalies in real time, while ITTIA DB Lite AI persistently stores and organizes the data and results directly on the device.
By combining AI with deterministic data management, raw environmental signals are transformed into meaningful insights. Anomaly frequency, duration, and severity are analyzed to generate real-time environmental condition scores, along with on-device statistics such as average temperature, peak wind speed, pressure trends, and anomaly counts.
By the end of the session, participants build a complete Edge AI solution that demonstrates how intelligent data processing and on-device AI enable reliable, explainable, and real-time environmental monitoring systems directly at the edge.
Course Details
Workshop Cost: USD $250 per attendee
The workshop fee includes:
- Comprehensive lecture materials
Workstation: This is a hands-on workshop. You must have an STM32H573I-DK device, a USB-C cable, and a Windows PC:
*Unlock 25% OFF your STM32H573I order when you register!
Edge Data AI Certification for Embedded Devices
Workshop attendees will receive ITTIA DB Platform certification. This certification validates hands-on expertise in using the ITTIA DB Platform for deterministic data management, on-device data processing, and Edge AI–ready data handling. It demonstrates and validates the ability to design reliable, power-fail-safe, and AI-enabled embedded systems using proven data architectures, reducing development risk, accelerating time to production, and enabling high-quality, scalable solutions.
Past Workshop Participants
ITTIA has delivered highly sought-after, hands-on workshops that attract engineers and innovation leaders from across multiple sectors. Our sessions are trusted by companies building real-world, data- and AI-driven embedded systems, and have been attended by teams from leading organizations committed to advancing intelligent edge technologies, including:
Abbott – Acuity Brands – Advances Energy – Applied materials – Avery Biomedical Devices, Inc. – Badger Meter – Cardios – Cat Wranglers
Cognosos Inc. – Continental AG – CPI – Declarative Futures – EDS – Emtech – Everspin – EXFO – Honeywell – Kellogg Northwestern
L3Harris – Magna International – Mayo clinic – Megavolt Labs – Milwaukee Tool – Mold Masters – Nidec Global Appliances – OTTO Engineering
Philips – Resideo – Schneider Electric – Siemens – Southland Sensing Ltd. – TITOMA – TTI Floor Care – Wind River – Zetron/Codan
Audience
These immersive, hands-on workshops are built for embedded architects and engineers who are shaping the next generation of data-driven, AI-enabled edge systems and want more than theory, they want results. Through practical, expert-led sessions, participants learn how to select the right device platform, design deterministic real-time firmware, and implement robust on-device data management that elevates data to a first-class system asset. The program goes further by showing how to prepare and integrate optimized AI pipelines directly into embedded workflows, while addressing cybersecurity, functional safety, diagnostics, and long-term lifecycle management from day one. By the end of the workshop, engineers leave with the confidence, skills, and real-world experience needed to build intelligent, trustworthy, and future-proof embedded systems ready for production.
These workshops are highly technical, intended for professional engineers and are not suitable for the general public without relevant qualifications.
