Introduction to On-Device AI

As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Billions of mobile and other edge devices are ready to run optimized AI models. 

This course equips you with key skills to deploy AI on device:

• Explore how deploying models on device reduces latency, enhances efficiency, and preserves privacy.
• Go through key concepts of on-device deployment such as neural network graph capture, on-device compilation, and hardware acceleration.
• Convert pretrained models from PyTorch and TensorFlow for on-device compatibility.
• Deploy a real-time image segmentation model on device with just a few lines of code.
• Test your model performance and validate numerical accuracy when deploying to on-device environments
• Quantize and make your model up to 4x faster and 4x smaller for higher on-device performance.
• See a demonstration of the steps for integrating the model into a functioning Android app.

Learn from Krishna Sridhar, Senior Director of Engineering at Qualcomm, who has played a pivotal role in deploying over 1,000 models on devices and, with his team, has created the infrastructure used by over 100,000 applications.

By learning these techniques, you’ll be positioned to develop and deploy AI to billions of devices and optimize your complex models to run efficiently on the edge.

This course is designed for beginner AI developers, ML engineers, data scientists, and mobile developers looking to deploy optimized models on edge devices. Familiarity with Python, as well as PyTorch or TensorFlow is recommended.