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Course Outline

Foundations of Edge AI

  • Core definitions and key concepts
  • Distinctions between Edge AI and cloud-based AI
  • Advantages and typical use cases
  • Overview of available edge devices and platforms

Configuring the Edge Environment

  • Introduction to hardware such as Raspberry Pi and NVIDIA Jetson
  • Installation of required software and libraries
  • Setup of the development workspace
  • Hardware preparation for AI workloads

Building AI Models for Edge Deployment

  • Survey of machine learning and deep learning architectures suitable for edge
  • Training methodologies in both local and cloud settings
  • Optimization techniques like quantization and pruning
  • Essential tools and frameworks including TensorFlow Lite and OpenVINO

Implementing AI on Edge Hardware

  • Deployment workflows for diverse edge devices
  • Managing real-time data processing and inference
  • Monitoring and maintaining deployed models
  • Illustrative examples and industry case studies

Practical Applications and Projects

  • Creating AI apps for edge use cases, such as computer vision and NLP
  • Project: Constructing a smart camera system
  • Project: Implementing voice recognition on edge devices
  • Group collaborations focused on real-world scenarios

Assessing and Optimizing Performance

  • Methods for benchmarking model performance on edge hardware
  • Utilities for monitoring and troubleshooting Edge AI apps
  • Strategies to boost model efficiency
  • Mitigating challenges related to latency and power usage

Integration with IoT Ecosystems

  • Linking Edge AI solutions with IoT sensors and devices
  • Understanding communication protocols and data flows
  • Designing complete Edge AI and IoT architectures
  • Practical integration demonstrations

Ethics and Security in Edge AI

  • Safeguarding data privacy and security in Edge AI contexts
  • Mitigating bias and ensuring fairness in models
  • Adhering to regulatory standards and compliance
  • Best practices for responsible AI deployment

Capstone Projects and Exercises

  • Development of a full-scale Edge AI application
  • Application of concepts to real-world problems
  • Collaborative group activities
  • Project reviews and instructor feedback

Requirements

  • Basic understanding of artificial intelligence and machine learning concepts
  • Proficiency in programming languages, with a recommendation for Python
  • Familiarity with edge computing fundamentals

Target Audience

  • Software Developers
  • Data Scientists
  • Technology Enthusiasts
 14 Hours

Number of participants


Price per participant

Open Training Courses require 5+ participants.

Testimonials (3)

I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped

Nola - Laramie County Community College
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

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zaklad Uslugowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python

Upcoming Courses

Building AI Solutions on the Edge

2026-10-15 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Building AI Solutions on the Edge

2026-10-29 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Building AI Solutions on the Edge

2026-11-12 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Building AI Solutions on the Edge

2026-11-26 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

Building AI Solutions on the Edge

2026-12-10 09:30
14 hours
Oslo
3000 EUR (Online)
3400 EUR (Classroom)

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