Build On-Device Ai

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

What you'll 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

Requirements

Basic Python knowledge is recommended, but no prior AI experience is required

Description

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!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: On-Device Introduction & Setup

Lecture 2 On-Device Introduction

Lecture 3 Qualcomm AI Hub Introduction

Lecture 4 Qualcomm AI Hub Login

Section 3: Model Training & Deployment Steps

Lecture 5 Steps for On-Device Deployment

Lecture 6 Model Training Phase - Theory

Lecture 7 Training the Model- Practical

Section 4: Model Compilation & Profiling

Lecture 8 Compiling the Model - Theory

Lecture 9 Compiling the Model - Practical

Lecture 10 Profiling the Model - Theory

Lecture 11 Profiling the Model - Practical

Section 5: Model Inference & Deployment

Lecture 12 Inference

Lecture 13 Downloading the Model

Section 6: Model Optimization & Quantization

Lecture 14 Introduction to Quantization

Lecture 15 Symmetrics quantization

Lecture 16 Asymmetrics Quantization

Lecture 17 Quantization Techniques - Practical Application

Section 7: Conclusion

Lecture 18 About your certificate

Lecture 19 Bonus lecture

Beginners in machine learning looking to gain hands-on experience in model optimization and on-device AI deployment,AI professionals, data scientists, and students who want to optimize models for deployment on resource-constrained devices like mobile, IoT, and embedded systems,Developers and engineers interested in learning how to use Qualcomm AI Hub to compile, profile, and deploy efficient AI models