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Duration 14 hours
Course Outline
Introduction to Edge AI
- Definitions and fundamental concepts
- Distinctions between Edge AI and cloud AI
- Advantages and application scenarios of Edge AI
- Overview of available edge devices and platforms
Configuring the Edge Environment
- Overview of edge devices (such as Raspberry Pi, NVIDIA Jetson, etc.)
- Installation of required software and libraries
- Setup of the development environment
- Preparing hardware for AI deployment
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 environments
- Model optimization for edge deployment (including quantization and pruning)
- Tools and frameworks for Edge AI development (such as TensorFlow Lite and OpenVINO)
Deploying AI Models on Edge Hardware
- Procedures for deploying AI models on various edge hardware
- Real-time data processing and inference on edge devices
- Monitoring and managing deployed models
- Practical examples and case studies
Practical AI Solutions and Projects
- Creating AI applications for edge devices (e.g., computer vision, natural language processing)
- Hands-on project: Building a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Collaborative group projects and real-world scenarios
Performance Assessment and Optimization
- Methods for evaluating model performance on edge devices
- Tools for monitoring and debugging edge AI applications
- Strategies for enhancing AI model performance
- Mitigating latency and power consumption issues
Integration with IoT Systems
- Linking edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange techniques
- Constructing a complete Edge AI and IoT solution
- Practical integration examples
Ethical and Security Considerations
- Safeguarding data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Compliance with relevant regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Real-world projects and scenarios
- Collaborative group exercises
- Project presentations and feedback sessions
Requirements
- A solid grasp of AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Knowledge of edge computing fundamentals
Target Audience
- Developers
- Data scientists
- Tech enthusiasts
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
