If you are a developer, data scientist, or AI enthusiast looking to create deployment-ready efficient AI models for edge devices, this course is for you. Do you want to accelerate AI inference while reducing computational overhead? Are you looking for practical techniques to optimize your models for mobile, IoT, and embedded systems?

This course will teach you how to train, compile, profile, and optimize AI models, ensuring they run efficiently on resource-constrained devices without compromising performance.

In this course, you will:

1. Learn the complete workflow of On-Device AI Deployment – from training to inference.
2. Understand Qualcomm AI Hub and how to use it for AI model management.
3. Explore model compilation and profiling to enhance performance.
4. Implement inference techniques for deploying models on edge devices.
5. Master quantization techniques to optimize AI models for low-power hardware.

Why Learn On-Device AI?

Deploying AI on edge devices allows you to reduce latency, enhance privacy, and optimize performance without depending on cloud computing. By mastering quantization, model profiling, and efficient AI deployment, you can ensure your models run faster, consume less power, and are ready for real-world applications like mobile AI, autonomous systems, and IoT.

Throughout the course, you'll gain hands-on experience with real-world AI deployment scenarios. You will balance theory and practical application to make your models leaner, smarter, and deployment-ready.

By the end of the course, you'll be equipped with the skills to train, optimize, and deploy AI models on edge devices, making you a valuable asset in the field of AI deployment.

Ready to take your AI models to the next level? Enroll now and start your journey!