Course Outline
Introduction to Edge AI
- Core definitions and concepts
- Distinguishing Edge AI from cloud-based AI
- Key benefits and common use cases
- Survey of available edge devices and platforms
Configuring the Edge Environment
- Overview of edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Setup of the development environment
- Hardware preparation for AI integration
Building AI Models for the Edge
- Overview of machine learning and deep learning models suitable for edge devices
- Methods for training models in local and cloud settings
- Optimization techniques for edge deployment (such as quantization and pruning)
- Key tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models on Edge Hardware
- Process for deploying AI models across different edge devices
- Handling real-time data processing and inference on the edge
- Oversight and management of deployed models
- Illustrative examples and case studies
Practical AI Applications and Projects
- Creating AI applications for edge devices (e.g., computer vision, NLP)
- Project: Constructing a smart camera system
- Project: Integrating voice recognition on edge hardware
- Group collaboration on real-world scenarios
Performance Assessment and Refinement
- Methods for assessing model performance on edge devices
- Utilizing tools for monitoring and debugging Edge AI apps
- Tactics for optimizing AI model efficiency
- Mitigating challenges related to latency and power usage
Integration with IoT Systems
- Connecting Edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange methods
- Constructing an end-to-end Edge AI and IoT solution
- Practical integration demonstrations
Ethical and Security Perspectives
- Safeguarding data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Adhering to relevant regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Working on real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving feedback
Requirements
- A solid grasp of AI and machine learning fundamentals
- Proficiency in programming languages (Python is preferred)
- Knowledge of edge computing principles
Target Audience
- Software Developers
- Data Scientists
- Tech Enthusiasts
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 3200 € + VAT*
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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