AI on Embedded Platforms using Raspberry Pi
Duration – 5 Days
Objectives
- Understand AI concepts tailored for embedded systems
- Interface sensors and cameras with Raspberry Pi
- Train and deploy ML models using TensorFlow Lite
- Build real-time AI applications for automation and computer vision
Tools & Platforms
- Hardware: Raspberry Pi 4, DHT11, LDR, MCP3008, Pi Camera, LEDs
- Software: Raspberry Pi OS, Python, TensorFlow Lite, OpenCV, Matplotlib
- Languages: Python (with libraries for ML, vision, and GPIO control)
Pre-requisites
- Basic Python programming
- Fundamentals of machine learning
- Familiarity with electronic components (GPIO, sensors)
Take away
- Complete knowledge of deploying AI on Raspberry Pi
- Build end-to-end sensor-based and vision-based AI systems
- Hands-on experience in real-time AI inference and automation
- Final project integrating sensors and computer vision
Day 2: Sensor Interfacing and Data Acquisition
Theory:
- Interfacing digital and analog sensors
- Working with ADC (MCP3008) on Raspberry Pi
- Structured data logging and file formats
- Fundamentals of Exploratory Data Analysis (EDA)
- Preprocessing techniques for AI readiness
Hands-On:
- Connect and read data from DHT11 (temperature & humidity)
- Use MCP3008 with LDR to capture analog light data
- Automate sensor data logging with timestamps
- Save sensor readings to CSV and visualize using matplotlib
Day 3: Developing AI Models for Embedded Systems
Theory:
- Overview of Supervised Learning: Classification vs Regression
- Sensor data as features – feature engineering
- Selecting lightweight models for embedded deployment
- Evaluation techniques: Accuracy, precision, recall, etc.
Hands-On:
- Preprocess collected sensor data
- Train ML models (SVM, Decision Tree, Random Forest)
- Evaluate model performance
Day 4: Real-Time Inference and Automation
Theory:
- TensorFlow Lite runtime and efficient inference
- Challenges in latency and real-time data processing
- Resource optimization and memory management
- Error handling and system reliability on edge
Hands-On:
- Load and run. tflite models on Raspberry Pi
- Integrate live sensor inputs into AI inference pipeline
- Trigger GPIO actions based on predictions (e.g., LED control)
- Optimize AI inference performance
- Complete sensor-to-decision ML system deployment
Day 5: AI-Driven Computer Vision on Raspberry Pi
Theory:
- Introduction to Embedded Computer Vision
- Optimizing images for embedded inference
- Choosing between pre-trained and custom-trained models
- Real-time object classification vs detection
Hands-On:
- Setup Pi Camera and stream live video
- Deploy MobileNet with TensorFlow Lite for object detection
- Perform image preprocessing for better accuracy
- Draw bounding boxes and display real-time predictions
- Trigger actions based on visual inputs
- Final project: Combine sensor and camera data for AI-powered automation
