Building AI Solutions on the Edge

• An understanding of AI and machine learning concepts
• Experience with programming languages (Python recommended)
• Familiarity with edge computing concepts

Audience

• Developers
• Data scientists
• Tech enthusiasts

Building AI Solutions on the Edge focuses on the step-by-step creation and deployment of AI models on edge devices. This course includes practical projects and real-world applications, providing participants with hands-on experience in developing and implementing AI solutions directly on edge hardware.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

By the end of this training, participants will be able to:

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

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.

Introduction to Edge AI

• Definition and key concepts
• Differences between Edge AI and cloud AI
• Benefits and use cases of Edge AI
• Overview of edge devices and platforms

Setting Up the Edge Environment

• Introduction to edge devices (Raspberry Pi, NVIDIA Jetson, etc.)
• Installing necessary software and libraries
• Configuring the development environment
• Preparing the hardware for AI deployment

Developing AI Models for the Edge

• Overview of machine learning and deep learning models for edge devices
• Techniques for training models on local and cloud environments
• Model optimization for edge deployment (quantization, pruning, etc.)
• Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)

Deploying AI Models on Edge Devices

• Steps 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

• Developing 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 Evaluation and Optimization

• Techniques for evaluating model performance on edge devices
• Tools for monitoring and debugging edge AI applications
• Strategies for optimizing AI model performance
• Addressing latency and power consumption challenges

Integration 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 integration examples

Ethical and Security Considerations

• Ensuring data privacy and security in Edge AI applications
• Addressing bias and fairness in AI models
• Compliance with 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

Summary and Next Steps