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Duration 14 hours
Course Outline
Foundations of Edge AI
- Defining Edge AI and exploring its core concepts
- Distinguishing between Edge AI and cloud-based AI
- Highlighting the benefits and typical use cases of Edge AI
- Surveying available edge devices and platforms
Configuring the Edge Environment
- Overview of edge devices such as Raspberry Pi and NVIDIA Jetson
- Installation of essential software and libraries
- Setting up the development environment
- Preparing hardware for AI workloads
Building AI Models for Edge
- Exploring machine learning and deep learning models suitable for edge devices
- Methods for training models in both local and cloud settings
- Optimizing models for edge use (e.g., quantization, pruning)
- Utilizing key tools and frameworks like TensorFlow Lite and OpenVINO
Deploying AI on Edge Hardware
- Procedures for deploying AI models across different edge hardware
- Performing real-time data processing and inference on edge devices
- Monitoring and managing live models
- Reviewing practical examples and case studies
Implementing Practical AI Solutions
- Creating AI applications for edge devices (e.g., computer vision, NLP)
- Practical project: Constructing a smart camera system
- Practical project: Deploying voice recognition on edge devices
- Collaborative group projects based on real-world scenarios
Assessing and Optimizing Performance
- Techniques for measuring model performance on edge devices
- Using tools to monitor and debug edge AI applications
- Strategies for enhancing AI model efficiency
- Managing latency and power consumption constraints
Integrating with IoT Systems
- Linking edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange
- Constructing a complete Edge AI and IoT solution
- Examining practical integration examples
Ethics and Security in AI
- Protecting data privacy and security in Edge AI contexts
- Mitigating bias and ensuring fairness in AI models
- Meeting regulatory and standard compliance requirements
- Adopting best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Working on real-world projects and scenarios
- Engaging in collaborative group exercises
- Presenting projects and receiving feedback
Requirements
- A solid foundation in AI and machine learning concepts
- Proficiency in programming languages, with a focus on Python
- 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 2600 € + VAT*
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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
