• views: 134
• date: 26 February 2025
• posted by: voska89

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

Homepage:

https://www.udemy.com/course/build-on-device-ai/

   
   
   

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