Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Edge AI
- Definition and key concepts.
- Differences between Edge AI and Cloud AI.
- Benefits and challenges of Edge AI.
- Overview of Edge AI applications.
Edge AI Architecture
- Components of Edge AI systems.
- Hardware and software requirements.
- Data flow in Edge AI applications.
- Integration with existing systems.
Setting Up the Edge AI Environment
- Introduction to Edge AI platforms (Raspberry Pi, NVIDIA Jetson, etc.).
- Installing necessary software and libraries.
- Configuring the development environment.
- Initializing the Edge AI setup.
Developing Edge AI Models
- Overview of machine learning and deep learning models for edge devices.
- Training models specifically for edge deployment.
- Techniques for optimising models for edge devices.
- Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.).
Data Management and Preprocessing for Edge AI
- Data collection techniques for edge environments.
- Data preprocessing and augmentation for edge devices.
- Managing data pipelines on edge devices.
- Ensuring data privacy and security in edge environments.
Deploying Edge AI Applications
- Steps for deploying models on various edge devices.
- Techniques for monitoring and managing deployed models.
- Real-time data processing and inference on edge devices.
- Case studies and practical examples of deployment.
Integrating Edge AI with IoT Systems
- Connecting Edge AI solutions with IoT devices and sensors.
- Communication protocols and data exchange methods.
- Building an end-to-end Edge AI and IoT solution.
- Practical examples and use cases.
Use Cases and Applications
- Industry-specific applications of Edge AI.
- In-depth case studies in healthcare, automotive, and smart homes.
- Success stories and lessons learned.
- Future trends and opportunities in Edge AI.
Ethical Considerations and Best Practices
- Ensuring privacy and security in Edge AI deployments.
- Addressing bias and fairness in Edge AI models.
- Compliance with regulations and standards.
- Best practices for responsible AI deployment.
Hands-On Projects and Exercises
- Developing a complex Edge AI application.
- Real-world projects and scenarios.
- Collaborative group exercises.
- Project presentations and feedback.
Summary and Next Steps
Requirements
- A solid understanding of basic AI and machine learning concepts.
- Experience with programming languages (Python is recommended).
- Familiarity with edge computing and IoT concepts.
Audience
- Developers.
- IT professionals.
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
