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 Duration 14 hours

Course Outline

Introduction to Edge AI

  • Defining key concepts and foundational principles
  • Distinguishing between Edge AI and cloud-based AI
  • Exploring the advantages and common use cases of Edge AI
  • An overview of available edge devices and platforms

Setting Up the Edge Environment

  • Familiarization with edge hardware such as Raspberry Pi and NVIDIA Jetson
  • Installation of essential software and libraries
  • Configuration of the development environment
  • Hardware preparation for AI workload deployment

Developing AI Models for the Edge

  • Reviewing machine learning and deep learning architectures suited for edge devices
  • Methods for training models in both local and cloud settings
  • Optimizing models for edge efficiency through techniques like quantization and pruning
  • Utilizing development tools and frameworks such as TensorFlow Lite and OpenVINO

Deploying AI Models on Edge Devices

  • Processes for deploying AI models across different types of edge hardware
  • Managing real-time data processing and inference on edge nodes
  • Monitoring and maintaining deployed models
  • Reviewing practical examples and case studies

Practical AI Solutions and Projects

  • Creating AI applications for edge devices, including computer vision and natural language processing
  • Project: Constructing a smart camera system
  • Project: Implementing voice recognition capabilities on edge hardware
  • Collaborative group projects simulating real-world scenarios

Performance Evaluation and Optimization

  • Techniques for assessing model performance on edge constraints
  • Using tools for monitoring and debugging Edge AI applications
  • Strategies for enhancing AI model efficiency
  • Mitigating challenges related to latency and power consumption

Integration with IoT Systems

  • Linking Edge AI solutions with IoT devices and sensors
  • Understanding communication protocols and data exchange mechanisms
  • Constructing comprehensive end-to-end Edge AI and IoT solutions
  • Exploring practical integration examples

Ethical and Security Considerations

  • Safeguarding data privacy and security within Edge AI frameworks
  • Mitigating bias and ensuring fairness in AI model outputs
  • Adhering to relevant regulations and industry standards
  • Adopting best practices for responsible AI deployment

Hands-On Projects and Exercises

  • Building a comprehensive Edge AI application
  • Engaging with real-world projects and scenarios
  • Participating in collaborative group exercises
  • Presenting projects and receiving constructive feedback

Requirements

  • A solid understanding of AI and machine learning fundamentals
  • Proficiency in programming languages (Python is recommended)
  • Basic familiarity with edge computing principles

Intended Audience

  • Developers
  • Data scientists
  • Tech enthusiasts

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Testimonials (2)

Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.

Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Upcoming Courses

Building AI Solutions on the Edge

2026-12-28 09:30
14 hours
Szeged
540 EUR (Online)
940 EUR (Classroom)

Building AI Solutions on the Edge

2027-01-11 09:30
14 hours
Kecskemét
540 EUR (Online)
940 EUR (Classroom)

Building AI Solutions on the Edge

2027-01-25 09:30
14 hours
Pécs, Belváros
540 EUR (Online)
940 EUR (Classroom)

Building AI Solutions on the Edge

2027-02-08 09:30
14 hours
Debrecen
540 EUR (Online)
940 EUR (Classroom)

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