Edge AI: From Concept to Implementation Training Course
Edge AI is the deployment and operation of AI models directly on edge devices, such as smartphones, IoT devices, and sensors, enabling real-time data processing and decision-making.
This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI from concept to practical implementation, including setup and deployment.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of Edge AI.
- Set up and configure Edge AI environments.
- Develop, train, and optimize Edge AI models.
- Deploy and manage Edge AI applications.
- Integrate Edge AI with existing systems and workflows.
- Address ethical considerations and best practices in Edge AI implementation.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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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 optimizing 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
- An understanding of basic AI and machine learning concepts
- Experience with programming languages (Python recommended)
- Familiarity with edge computing and IoT concepts
Audience
- Developers
- IT professionals
14 Hours
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