Edge AI & TinyML: Deploying AI on Edge Devices Training Course

Introduction

The proliferation of IoT devices and the demand for real-time intelligence are driving Artificial Intelligence (AI) away from the cloud and directly to the source of data generation – the edge. This Edge AI & TinyML: Deploying AI on Edge Devices Training Course is specifically designed for AI/ML engineers, embedded systems developers, IoT developers, hardware engineers, and data scientists eager to master the art of deploying AI models directly onto resource-constrained devices. The course delves into Edge AI, which encompasses running AI on devices like smartphones and gateways, and its specialized subset, TinyML, which focuses on ultra-low-power microcontrollers.

Participants will gain hands-on expertise in optimizing AI models for size and efficiency (e.g., quantization, pruning), selecting appropriate edge hardware, and utilizing specialized frameworks like TensorFlow Lite and PyTorch Mobile. The curriculum covers practical applications across computer vision, audio processing, and sensor data analytics, emphasizing the unique challenges of on-device inference, power consumption, and real-time processing. By understanding the complete Edge AI lifecycle, including deployment, monitoring, and security, you will be equipped to build and scale intelligent applications that operate autonomously, privately, and efficiently at the very edge of the network.

Target Audience

• AI/Machine Learning Engineers.
• Embedded Systems Developers.
• IoT Developers and Architects.
• Hardware Engineers and Chip Designers.
• Data Scientists looking to deploy models on devices.
• Product Managers in IoT, Consumer Electronics, and Industrial Automation.

Duration

10 days

Course Objectives

1. Understand the core concepts, benefits, and challenges of Edge AI and TinyML.
2. Master techniques for optimizing and compressing AI models for resource-constrained edge devices.
3. Gain practical experience in deploying AI models on various edge hardware, including microcontrollers (TinyML) and single-board computers.
4. Learn to utilize popular Edge AI development frameworks like TensorFlow Lite and PyTorch Mobile.
5. Implement AI solutions for common edge applications, such as computer vision, audio processing, and sensor data analysis.
6. Understand the MLOps principles tailored for managing the lifecycle of AI models on edge devices.
7. Address security, privacy, and ethical considerations inherent in Edge AI deployments.
8. Explore real-world use cases and future trends in the rapidly expanding field of Edge AI.

Course Content

Module 1. Introduction to Edge AI & TinyML

• What is Edge Computing? Cloud vs. Edge vs. Fog computing
• What is Edge AI? Benefits (latency, bandwidth, privacy, reliability) and challenges
• What is TinyML? Running ML on ultra-low-power microcontrollers
• Use cases across industries: Smart home, industrial IoT, wearables, automotive
• The economic and environmental impact of Edge AI

Module 2. Fundamentals of Edge Devices & IoT

• Overview of edge hardware architectures: Microcontrollers (MCUs), Microprocessors (MPUs), FPGAs, ASICs
• Key characteristics of edge devices: Compute power, memory, energy consumption
• Types of sensors and actuators for edge AI applications
• Introduction to IoT architectures and protocols relevant to edge deployments
• Choosing the right hardware for specific Edge AI tasks

Module 3. AI Model Optimization Techniques

• The necessity of model compression for edge deployment
• Model quantization: Reducing precision (e.g., float32 to int8)
• Model pruning: Removing redundant weights and connections
• Knowledge distillation: Transferring knowledge from a large model to a smaller one
• Neural architecture search (NAS) for efficient edge models
• Tools for model optimization and conversion

Module 4. Frameworks for Edge AI Development

• TensorFlow Lite: Converting and deploying TensorFlow models for mobile and edge
• PyTorch Mobile: Optimizing and deploying PyTorch models for mobile and edge
• ONNX Runtime: Cross-platform inference engine for various ML frameworks
• Edge Impulse: An end-to-end development platform for TinyML
• Comparing framework capabilities and ecosystem support

Module 5. Deploying AI on Microcontrollers (TinyML)

• Understanding the extreme resource constraints of MCUs (kilobytes of RAM)
• Toolchains and development environments for TinyML (e.g., Arduino IDE, PlatformIO)
• Hands-on: Building and deploying a simple classification model on an Arduino or ESP32 board
• Keyword spotting with TinyML (e.g., "Hey Google" on device)
• Visual wake words and simple image classification on microcontrollers

Module 6. Deploying AI on Edge Gateways & SBCs

• Single Board Computers (SBCs): Raspberry Pi, NVIDIA Jetson, Coral Dev Board
• Edge Gateways: Bridging IoT devices to the cloud, local processing
• Containerization (Docker) and virtualization for managing AI workloads on gateways
• Deploying complex models (e.g., object detection) on more powerful edge devices
• Hands-on: Setting up an edge device for computer vision inference

Module 7. Edge AI for Computer Vision Applications

• Real-time object detection and classification on edge devices
• Facial recognition and emotion detection (with ethical considerations)
• Pose estimation and gesture recognition for human-computer interaction
• Visual anomaly detection for industrial inspection
• Optimizing CNNs for edge deployment: MobileNet, EfficientNet variants

Module 8. Edge AI for Audio & Sensor Data

• Keyword spotting and voice command recognition on low-power devices
• Sound event detection (e.g., glass breaking, alarms)
• Anomaly detection from sensor data for predictive maintenance
• Time-series forecasting on edge for energy or environmental monitoring
• Feature extraction techniques for audio and sensor data for TinyML

Module 9. Edge AI Lifecycle Management (MLOps for Edge)

• Data collection and labeling strategies for edge scenarios
• Model versioning and tracking for edge deployments
• Over-the-Air (OTA) updates for models and firmware on edge devices
• Monitoring edge model performance, data drift, and concept drift
• Challenges of MLOps for decentralized edge deployments

Module 10. Security & Privacy in Edge AI

• Data locality and privacy by design in Edge AI
• Securing edge devices from attacks and unauthorized access
• Protecting AI models on devices: Tamper detection, IP protection
• Federated Learning: Collaborative model training without centralizing data
• Homomorphic encryption and differential privacy for enhanced edge privacy

Module 11. Edge AI Hardware Accelerators

• Specialized AI chips: TPUs (Tensor Processing Units), NPUs (Neural Processing Units)
• Advantages of hardware accelerators for AI inference at the edge
• Evaluating power consumption vs. performance trade-offs
• Overview of leading edge AI accelerator vendors and their offerings
• Designing for energy efficiency in embedded AI systems

Module 12. Real-World Applications & Future of Edge AI

• Smart home automation: Voice control, security cameras, energy management
• Industrial IoT: Predictive maintenance, quality control, worker safety
• Autonomous systems: Drones, robotics, self-driving vehicles
• Personalized healthcare: Wearable health monitors, remote diagnostics
• The future of Edge AI: Generative AI on edge, ubiquitous intelligence, human-AI collaboration

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank