Course Description

This course provides comprehensive training on deploying Artificial Intelligence (AI) models on edge devices such as smartphones, IoT devices, and AR/VR headsets. It focuses on utilizing the local compute capabilities of these devices to achieve faster response times and enhanced data privacy. Instruction covers model conversion for device compatibility, quantization techniques for improved performance, and real-world implementation strategies.

What Students Will Learn

• Techniques to reduce latency and increase efficiency through on-device AI deployment.
Methods for converting and optimizing PyTorch and TensorFlow models to run effectively on edge devices.
• Details on integrating AI models with mobile devices, focusing on runtime dependencies and compute unit utilization.
• Processes to quantize AI models, increasing their performance and decreasing their size significantly.
• Steps for real-world application through a case study involving an image segmentation model deployment on a smartphone.
• Strategies to test and validate models directly on device environments, ensuring operational fidelity.

Prerequisites

Participants are expected to have a basic understanding of AI concepts and be familiar with Python programming. Experience with machine learning frameworks such as PyTorch or TensorFlow is also recommended. This foundation will be crucial in understanding the course content and actively engaging in model conversion and deployment exercises.

Course Outline

• Introduction to AI model deployment on edge devices.
• Conversion of pre-trained PyTorch and TensorFlow models for device compatibility.
• In-depth analysis of hardware acceleration techniques like GPU, NPU, and CPU utilization.
• Quantization of models to improve speed and reduce size.
• Deployment of an image segmentation model on an Android device.
• Testing and validation of on-device model performance.
• Integration of AI models into real-world mobile applications.

Who Should Enroll

This course is ideal for beginner AI developers, machine learning engineers, data scientists, and mobile developers who aspire to master the skill of deploying optimized AI models to edge devices. Those interested in enhancing their AI applications’ performance by leveraging on-device computation will find this training particularly beneficial.

Real-World Application of Skills

Upon completing this course, learners will be equipped to apply their skills in various sectors needing localized AI solutions, such exceptional responsiveness, and stringent data privacy are essential. Uses include developing smarter mobile apps, improving IoT systems, boosting performance in robotics, and enriching experiences in AR/VR applications.