Smart Edge AI: From TinyML Foundations to Context-Aware Multi-Sensor Systems
TensorFlow Lite, Edge Impulse (Export), MicroPython, Arduino Nano Simulator, Google Colab
Skills you will gain:
About Program:
Stealth AI: Embedded & Ambient Machine Learning is a specialized international workshop that explores the intersection of artificial intelligence, IoT, and ubiquitous computing. It introduces the design and deployment of intelligent models that run on edge devices (like microcontrollers, wearables, and sensors) and ambient systems (like smart homes and environments).
The workshop bridges the gap between traditional machine learning and embedded systems by emphasizing TinyML, on-device AI, context recognition, and privacy-preserving computation, all with real-world applications in healthcare, defense, consumer electronics, and smart infrastructure.
Aim: To empower participants with cutting-edge knowledge and practical experience in Embedded and Ambient Machine Learning, focusing on building low-power, real-time, and context-aware AI systems that operate discreetly across edge devices and smart environments.
Program Objectives:
- Introduce the concepts of embedded and ambient ML
- Provide hands-on training in tools like TensorFlow Lite, Edge Impulse, and microcontroller SDKs
- Enable participants to build stealth AI applications that operate without cloud reliance
- Promote energy-efficient, ethical, and privacy-first AI design
- Prepare learners to innovate in the fields of smart environments and wearable intelligence
What you will learn?
📍 Day 1: TinyML & the Edge AI Ecosystem
Focus: Foundations & Toolchain
● What is Stealth AI? Applications in wearables, ambient sensors, IoT
● Edge AI vs Cloud AI: Power, latency, privacy trade-offs
● Overview: TensorFlow Lite, Edge Impulse, MicroPython, Arduino Nano
● Model optimization: Quantization, pruning, compression
🧪 Hands-On:
✔ Convert a trained model to .tflite using TensorFlow Lite
✔ Simulate deployment constraints on Colab
📍 Day 2: Building & Simulating Tiny AI Systems
Focus: Real-World Model Creation
● Efficient data collection for edge devices
● Model design for keyword spotting / motion detection
● Edge Impulse: Dataset → DSP → Model → Deployment
🧪 Hands-On:
✔ Train & export a TinyML model via Edge Impulse
✔ Deploy and simulate on Arduino Nano logic using MicroPython (via Colab)
📍 Day 3: Multi-Sensor AI & Future of Ambient Intelligence
Focus: Context-Aware Decision Making
● Use cases: Gesture recognition, energy-saving automation, ambient health monitoring
● Sensor fusion & real-time anomaly detection
● Privacy-first, on-device ML & federated learning potential
🧪 Hands-On:
✔ Build a multi-sensor inference simulation (accelerometer + mic)
✔ Final demo: Real-time edge decision simulation on Colab
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!
Intended For :
- Students and researchers in AI, Embedded Systems, IoT, or Electronics
- Professionals in embedded design, smart devices, or ML applications
- Hardware engineers and firmware developers exploring AI capabilities
- Innovation leaders, startup founders, and R&D teams in tech hardware
Career Supporting Skills
Program Outcomes
- Understand embedded ML models and deployment strategies
- Learn to compress, optimize, and run ML models on low-power devices
- Build real-time, context-aware applications using ambient inputs
- Explore secure and private AI computation at the edge
- Develop a hands-on project that simulates ambient intelligence
- Receive a recognized certificate and project-based credentialing