This course is ideal for AI engineers, embedded systems developers, and data scientists aiming to implement AI at the edge for real-time applications.
Edge-AI & On-Device Inference Systems
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You’ll learn to:
Optimize AI models for edge deployment
Implement on-device inference for real-time decision-making
Use frameworks and tools for model compression and acceleration
Handle hardware constraints such as memory, compute, and power efficiency
Deploy AI models on IoT and edge devices
Yes. The program includes practical labs and projects, where participants deploy AI models on actual edge devices and simulate real-world inference scenarios.
Participants should have Python programming skills and basic AI knowledge.
Yes. Participants who successfully complete the course will receive a Florence Fennel Certificate of Completion in Edge-AI & On-Device Inference Systems.
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Sky-rocket your team’s productivity with our customized courses and expert guidance and hands on practice on projects that are essential for crucial real-world challenges at work.
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We empower your team to operate more efficiently, resulting in significant cost savings over time. Our commitment is to assist you in transforming your operations, ensuring lasting financial benefits that contribute to the long-term success of your organization.
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Experience dynamic learning that evolves with our best practices, breaking free from the confines of theory. We go beyond the books, placing emphasis on practical application—especially on live projects, the real game-changer in your professional journey.
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Our programs are designed to equip participants with the skills and knowledge necessary to navigate intricate challenges seamlessly, ensuring they emerge as proficient problem-solvers in the dynamic business landscape.
This course provides hands-on training in deploying and optimizing AI models for real-time inference on edge devices, enabling efficient AI operations close to data sources.
It’s designed for AI engineers, embedded systems developers, and data scientists looking to implement AI on edge and IoT devices.
You’ll gain expertise in: Model optimization and compression On-device inference and real-time AI deployment Handling hardware constraints for edge devices Using frameworks for edge AI deployment Building AI solutions for IoT and real-world applications
Yes, participants should have Python programming skills and basic AI knowledge.
The course is hands-on, with labs and projects to deploy AI models on edge devices and optimize them for real-time performance.
The total duration is 28 hours, including lectures, practical labs, and guided projects.
Yes. Participants will earn a Florence Fennel Certificate of Completion in Edge-AI & On-Device Inference Systems, validating their ability to implement AI at the edge effectively.
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