Title

Build On-Device AI

Master On-Device AI! Learn to Train, Compile and Profile AI Models for Edge Device deployement with Qualcomm AI Hub

Udemy

platform

English

language

Data Science

What you will learn

Understand the complete workflow of On-Device AI deployment, from training to inference

Learn how to use Qualcomm AI Hub for managing, compiling, and optimizing AI models

Master model profiling and compilation to enhance performance on edge devices

Learn quantization techniques to optimize AI models for mobile, IoT, and embedded systems

Understand the difference between symmetric and asymmetric quantization

Why take this course?

🌟 Course Title: Build On-Device AI with Qualcomm AI Hub

🚀 Headline: Master On-Device AI! Elevate Your AI Models for Edge Device Deployment

🎉 Course Description:

Are you a developer, data scientist, or an AI enthusiast eager to harness the full potential of AI right at the edge of technology? Whether you're aiming to accelerate AI inference on mobile, IoT, and embedded systems or simply looking to optimize your models for efficient deployment, this comprehensive course is your gateway to mastery.

What You'll Learn:

🎓 Complete Workflow Mastery: From training your AI models to deploying them for inference on edge devices, we cover the entire spectrum.
🛠️ Qualcomm AI Hub Proficiency: Dive into hands-on experience with one of the most powerful tools for AI model management and see how it can streamline your workflow.
🚀 Model Compilation and Profiling Excellence: Learn advanced techniques to profile and compile your models for optimal performance, ensuring that they run smoothly on constrained hardware.
🧠 Inference Techniques for Edge Deployment: Explore cutting-edge inference methods that are crucial for successful deployment of AI models on edge devices.
电池️ Quantization Skills: Master the art of quantization to optimize your AI models for low-power hardware, enabling longer battery life and reduced computational overhead.

Why On-Device AI?

On-device AI deployment offers significant advantages over cloud computing. It allows for:

⚡️ Reduced Latency: Get faster response times by processing data locally.
🔒 Enhanced Privacy: Keep sensitive data secure by handling it on the user's device.
🆚 Optimized Performance: Ensure your AI applications run efficiently without the need for powerful centralized servers.

Real-World Applications:

📱 Mobile AI: Create smart and responsive apps that can operate with minimal power consumption.
🤖 Autonomous Systems: Power autonomous vehicles, drones, and robots with high-performance AI models.
💼 IoT Devices: Make IoT devices smarter by equipping them with capable AI algorithms.

💖 Hands-On Learning Experience: This course is designed to provide a perfect blend of theoretical knowledge and practical application, ensuring that your AI models are leaner, smarter, and deployable across various edge devices and real-world scenarios.

By the End of This Course, You Will Be Able To:

Train AI models specifically for on-device deployment.
Optimize and prepare these models for efficient execution on edge devices.
Deploy your AI solutions in environments like mobile, IoT, and embedded systems confidently.

Are You Ready?

Enroll now to transform your AI models into highly optimized, low-power, real-time solutions! 🚀💡