Embedded AI & AIoT: Building Intelligent Edge Systems

Explore embedded machine learning across the edge spectrum, from TinyML on microcontrollers to powerful edge devices, and learn how training and inference enable on-device insights for IoT.

Compare edge and cloud deployments for edge ai across latency, privacy, connectivity, bandwidth, cost, and compute. Edge delivers millisecond latency and offline operation; cloud handles training and heavier models.

Explore how AIoT powers smart homes with climate control that learns occupancy patterns and saves energy, AI-enabled security, smart lighting, and voice assistants coordinating devices.

Learn to write clean, professional Python code with PEP 8 as a shared readability standard. Master naming conventions, indentation, line length, whitespaces, imports, and docstrings to ensure readable, maintainable code.

Master five common Python pitfalls—mutable default arguments, bare except clauses, shadowing built-ins, loop mutation, and late binding in closures—and apply fixes like None defaults, specific exception handling, and safe iteration.

Automate data tasks with python by processing csv and filtering json data in a lab that demonstrates csv module, dict reader, aggregation of total sales, and command-line argparse.

Learn to test csv processing with pytest, parse input/output with argparse, read csv via dictreader, write results, and filter engineers by salary using a list comprehension with json.dumps.

Identify hardware constraints, data challenges, model trade-offs, energy limits, and trust barriers in embedded AI, and apply mitigation techniques like quantization, pruning, and TinyML frameworks such as TensorFlow Lite Micro.

Explore data quality risks and edge design trade-offs, from data scarcity and labeling costs to drift and privacy, and examine quantization, pruning, size, speed, and updates for robust embedded AI.

Navigate the four iterative AIoT project phases: discovery, prototype and development, deployment, and maintenance, to design embedded AI systems, align with stakeholders, and monitor data shift in real environments.

Treat data collection as the foundation of the AIoT pipeline, ensuring consistent, varied, and clearly labeled data from motion, environmental, audio, and visual sensors.

Master preprocessing in the AIoT pipeline by transforming noisy raw sensor data into clean features for edge and cloud deployment, using noise filtering, windowing, normalization, and feature extraction.

Explore preprocessing tools across gui AutoML platforms like Edge Impulse Studio, Python libraries such as Pandas and NumPy, Skippy and Skykit Learn, and on-device dsp libraries to ensure reproducible deployments.

Validate on real target hardware with on-device checks for accuracy match, latency, memory, live sensor testing, edge cases, and stability to ensure production readiness.

Learn how machine learning analyzes sensor data from IoT devices—time-series, spectral, and multivariate inputs—to enable classification, regression, anomaly detection, forecasting, and clustering, and deploy models on embedded hardware.

Compare random forest and SVM for sensor classification by dataset size, feature space, and interpretability, favoring random forests for tabular data and embedded deployment, reserving SVM for small, complex datasets.

Explore how multi-layer perceptrons and 1D CNNs run on microcontrollers, and master pruning, quantization, and knowledge distillation for embedded aiot models with heart rate anomaly detection and activity recognition.

Use the decision framework to choose feature-based vs end-to-end learning for sensor apps, balancing classification or regression outputs, labeled windows, and interpretability.

Explore the SCM32 and ESP32 MCUs from STMicroelectronics and Espressif Systems, and apply four criteria—processing power, memory, power consumption, peripherals and connectivity—to choose the right MCU for your IoT project.

Explore embedded IDEs and toolchains like GCC ARM, Keil MDK, IAR, and PlatformIO. See how the IDE's code editor, build system, debugger, and flash tool convert C to running firmware.

Configure microcontroller projects with qbmx, set up GPIO pins, clocks, and labels, then complete a two-exercise lab to blink an LED, read a button, and implement an interrupt.

Configure microcontroller pins PB6, PB7, and user button with GPIO input pull-ups and LED outputs, and implement both polling and SysTick-driven timing to blink LEDs every two seconds.

Detect and fix data quality issues in embedded ai and aiot sensor data by spotting outliers with setScore, iqr, and mad, and filling gaps with interpolation, splines, or forward fill.

Compare Edge Impulse's end-to-end deployment workflow with a custom stack using CMSIS DSP and TensorFlow Lite Micro on STM32 hardware to choose the right embedded deployment path.

Identify activities like running, walking, and idle; label data in Edge Impulse, then train with labeled examples and upload streams to cloud via Edge Impulse data forwarder for ML-ready datasets.

Configure uart2 on the QMX project, set baud rate and pins, enable interrupt mode, then implement main.c to process received bytes and send data or inference samples over uart2.

Explore analog-to-digital conversion and sampling as the bridge from the physical to the digital world, covering ADC basics, Nyquist theorem, and the BME280’s shared ADC across temperature, pressure, and humidity.

Explore the bme280 sensor, a 3-in-1 environmental chip measuring temperature, pressure, and humidity in a tiny package with dual I2C and SPI interfaces, with ultra-low power.

Explore I2C and SPI interfaces for sensors, comparing two-wire I2C (SDA, SCL) with four-wire SPI (MOSI, MISO, CLK, chip select), noting up to 128 devices and 3.4 MHz.

Configure a sensor from its register map and datasheet by a 5-step process: choose full scale range, set the data rate, configure filters, write via I2C or SPI, and verify.

Unlock SPI flash memory by issuing the write enable command (0x06) to set the WEL bit; this enables writes or erases via CS, MOSI, and SCLK.

Explore the 128 megabit W25Q128 SPI flash memory, its chip select driven transactions, four commands (06, 03, 02, 05), and the three-phase write pattern with busy-bit monitoring.

Bridge cloud-scale training to edge deployment with Edge Impulse, TensorFlow Lite Micro, and STM32Cube AI, and explore embedded neural networks with quantization and pruning.

Navigate the five-stage model conversion pipeline—from training in TensorFlow/Keras to flashing native code on a microcontroller—and master quantization, converting 32-bit weights to 8-bit integers with minimal accuracy loss.

Reproduce the exact preprocessing on-device—windowing, fft, mel filter banks, log compression, and dct to yield mfccs—then run a quantized network and apply thresholding and temporal smoothing for post-processing.

Master the Edge Impulse CLI workflow for HAR, using the Daemon, Uploader, Data Forwarder, Run Impulse, and Blocks, and deploy the Arduino library with TFLM on STM32 and ESP32.

Collect and label raw accelerometer data to train a tinyML model for four activity classes—idle, standing, walking, and running—using Edge Impulse data forwarder for real-time ingestion.