Learn to deploy AI models on edge devices like smartphones

Enroll in the full course 👉 bit.ly/4bzZD7L
We’re excited to announce that Introduction to On-Device AI, a new short course made in collaboration with Qualcomm and taught by Krishna Sridhar, Senior Director of Engineering at Qualcomm, is live!
As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Over 6 billion mobile devices and billions of other edge devices are ready to run optimized AI models.
In this course, you’ll learn how to deploy AI models on edge devices using their local compute power for faster and more secure inference:
- 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.
Introduction to On-Device AI launches soon. Sign up for the waitlist and be the first to enroll!
Enroll in the full course 👉 bit.ly/4bzZD7L