Build On-Device AI
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!
Who this course is for:
- 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
Instructor
- 4.5 Instructor Rating
- 141,873 Reviews
- 1,950,823 Students
- 118 Courses
Start-Tech Academy is a technology-based Analytics Education Company and aims at Bringing Together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Founded by Abhishek Bansal and Pukhraj Parikh.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.