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 Duration 14 hours

Course Outline

Introduction to Edge AI

  • Defining Edge AI and exploring its core concepts
  • Distinguishing between Edge AI and cloud-based AI
  • Examining the advantages and primary use cases of Edge AI
  • Surveying available edge devices and platforms

Configuring the Edge Environment

  • Getting acquainted with edge hardware such as Raspberry Pi and NVIDIA Jetson
  • Installing required software stacks and libraries
  • Setting up the development environment
  • Preparing hardware infrastructure for AI workloads

Building AI Models for Edge Deployment

  • Reviewing machine learning and deep learning architectures suitable for edge devices
  • Methods for training models in local and cloud environments
  • Optimizing models for the edge using techniques like quantization and pruning
  • Leveraging tools and frameworks for Edge AI, including TensorFlow Lite and OpenVINO

Deploying AI on Edge Hardware

  • Processes for deploying AI models across various edge hardware configurations
  • Handling real-time data processing and inference at the edge
  • Monitoring and managing live deployed models
  • Reviewing practical examples and industry case studies

Practical Applications and Projects

  • Creating AI applications for edge devices in areas like computer vision and natural language processing
  • Hands-on project: Designing and building a smart camera system
  • Hands-on project: Implementing voice recognition capabilities on edge devices
  • Collaborative group projects simulating real-world scenarios

Evaluating and Optimizing Performance

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

Integrating with IoT Systems

  • Linking Edge AI solutions with IoT devices and sensor networks
  • Exploring communication protocols and data exchange mechanisms
  • Constructing end-to-end Edge AI and IoT solutions
  • Reviewing practical integration case studies

Ethics and Security in Edge AI

  • Safeguarding data privacy and security in Edge AI contexts
  • Mitigating bias and ensuring fairness in AI models
  • Ensuring compliance with relevant regulations and standards
  • Adopting best practices for responsible AI deployment

Capstone Projects and Practical Exercises

  • Developing a comprehensive, end-to-end Edge AI application
  • Working through real-world project scenarios
  • Engaging in collaborative group exercises
  • Presenting projects and receiving constructive feedback

Requirements

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

Target Audience

  • Developers
  • Data Scientists
  • Tech Enthusiasts

Number of participants


Price per participant (excl. VAT)

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

2026-11-25 09:30
14 hours
Dresden
3472 EUR (Online)
3872 EUR (Classroom)

Building AI Solutions on the Edge

2026-12-09 09:30
14 hours
Leipzig
3472 EUR (Online)
3872 EUR (Classroom)

Building AI Solutions on the Edge

2026-12-23 09:30
14 hours
Nuremberg
3472 EUR (Online)
3872 EUR (Classroom)

Building AI Solutions on the Edge

2027-01-06 09:30
14 hours
Hannover
3472 EUR (Online)
3872 EUR (Classroom)

Building AI Solutions on the Edge

2027-01-20 09:30
14 hours
Stuttgart
3472 EUR (Online)
3872 EUR (Classroom)

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