Edge AI and IoT: The Real-Time Revolution Training Course

The convergence of Edge AI and the Internet of Things (IoT) is creating a new paradigm for real-time data processing and intelligent decision-making. This course explores how to deploy artificial intelligence directly on IoT devices, bypassing the need for constant cloud connectivity. Participants will learn to build powerful, low-latency applications that enhance efficiency, security, and autonomy in a wide range of industries, from manufacturing to smart cities.

This training is designed to empower developers and engineers to overcome the challenges of traditional cloud-based AI. You'll gain hands-on experience in optimizing machine learning models for resource-constrained hardware, managing data privacy at the source, and ensuring the reliability of embedded systems. By the end of this program, you'll possess the skills to architect and implement robust Edge AI and IoT solutions that drive innovation and create a competitive advantage.

Duration

5 days

Target Audience

This course is intended for software developers, AI engineers, embedded systems programmers, data scientists, and solutions architects who want to specialize in the design and implementation of intelligent, real-time systems.

Course Objectives

  1. Understand the core concepts of Edge AI and its relationship with IoT.
  2. Learn to select appropriate hardware and software for Edge AI applications.
  3. Acquire skills in optimizing machine learning models for low-power devices.
  4. Master the deployment of AI models on various edge devices.
  5. Gain proficiency in data management and preprocessing for IoT environments.
  6. Understand and apply key security and privacy measures for Edge AI and IoT.
  7. Explore communication protocols and architectures for Edge-enabled IoT systems.
  8. Develop and implement real-time applications for various industries.
  9. Learn to troubleshoot and maintain deployed Edge AI models.
  10. Formulate an end-to-end strategy for developing and scaling Edge AI solutions.

Course Modules

Module 1: Introduction to Edge AI and IoT

  • Core principles and definitions of Edge AI and the IoT.
  • The benefits and challenges of integrating AI at the edge.
  • Comparison of Edge AI versus Cloud AI architectures.
  • Key use cases and applications across different sectors.
  • Overview of the hardware and software ecosystem for Edge AI.

Module 2: IoT Systems and Architectures

  • Fundamentals of IoT devices, sensors, and actuators.
  • Common IoT network topologies and communication protocols (e.g., MQTT, CoAP).
  • Data flow and processing in a typical IoT system.
  • Introduction to popular IoT platforms (e.g., Raspberry Pi, Arduino, NVIDIA Jetson).
  • Architecting scalable and resilient IoT systems.

Module 3: Setting Up the Edge AI Environment

  • Preparing the development hardware and operating system.
  • Installing necessary libraries and frameworks for Edge AI.
  • Configuring the device for sensor data collection.
  • Establishing a secure connection for data transmission.
  • Initializing a basic project structure for an AI application.

Module 4: Machine Learning Fundamentals for the Edge

  • Review of essential machine learning and deep learning concepts.
  • Understanding model types suitable for embedded systems.
  • Introduction to popular frameworks like TensorFlow Lite and PyTorch Mobile.
  • Training a basic model for a simple classification task.
  • Model evaluation and performance metrics.

Module 5: Model Optimization for Edge Devices

  • Techniques for model compression: pruning, quantization, and knowledge distillation.
  • Reducing model size without significant loss of accuracy.
  • Optimizing models for specific hardware accelerators.
  • Using tools like OpenVINO and TensorRT for high-performance inference.
  • Balancing accuracy, latency, and power consumption.

Module 6: Data Management and Preprocessing

  • Strategies for efficient data collection from IoT devices.
  • Real-time data preprocessing on the device.
  • Managing data streams and pipelines at the edge.
  • Data augmentation techniques for constrained datasets.
  • Ensuring data integrity and reliability.

Module 7: Deploying Models to the Edge

  • Step-by-step process of deploying a trained model.
  • Packaging the model and its dependencies for the target device.
  • Remote deployment and over-the-air (OTA) updates.
  • Monitoring the health and performance of deployed models.
  • Troubleshooting deployment failures.

Module 8: Edge AI and Computer Vision

  • Implementing object detection and classification on the edge.
  • Real-time video and image analysis for surveillance and security.
  • Optimizing vision models for limited compute power.
  • Hands-on project: building an intelligent camera system.
  • Case studies in retail and industrial automation.

Module 9: Edge AI and Natural Language Processing (NLP)

  • Deploying NLP models for voice commands and keyword spotting.
  • Creating on-device chatbots and voice assistants.
  • Using lightweight language models for text analysis.
  • Building a simple, real-time NLP application.
  • Exploring applications in smart home devices.

Module 10: Security and Privacy at the Edge

  • Threat modeling for Edge AI and IoT systems.
  • Securing data at rest and in transit on the device.
  • Implementing authentication and access control.
  • Protecting against model reverse-engineering and tampering.
  • Ethical considerations and compliance with data privacy regulations.

Module 11: Real-World Applications & Case Studies

  • Industrial IoT (IIoT) for predictive maintenance and quality control.
  • Smart cities: traffic management and public safety.
  • Healthcare: remote patient monitoring and medical imaging analysis.
  • Agriculture: smart farming and crop health monitoring.
  • Autonomous systems: robotics and self-driving vehicles.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Edge Ai And Iot: The Real-time Revolution Training Course in Namibia
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