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This Edge Computing In AItutorial will provide you with a comprehensive understanding of the integration of edge computing and artificial intelligence (AI). You will explore the fundamentals of edge computing and its significance in the context of AI, including reduced latency, improved privacy, and efficient network utilization. The tutorial will cover the deployment and execution of AI algorithms and models on edge devices, enabling real-time inference and decision-making at the network's edge. Additionally, you will learn about the challenges and considerations associated with edge computing for AI, such as resource constraints, security, and scalability. Practical examples and case studies from various domains, including healthcare and smart cities, will demonstrate the transformative potential of edge computing for AI applications. By the end of the tutorial, you will have a solid foundation in the principles and applications of edge computing for AI, enabling you to harness its power for intelligent and responsive systems.

Following are the topic covered in this tutorial:

00:00 Introduction to Edge AI
03:05 What is Edge Computing?
03:53 What Led to the Rise of Edge AI?
06:02 Why Deploy AI at the Edge?
07:45 How Does Edge Computing works?
10:17 Real-Life Applications of Edge AI

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➡️ About Artificial Intelligence Engineer

This Artificial Intelligence Engineer course Created in partnership with IBM, this course introduces students to blended learning and prepares them to be AI and Data Science specialists. In Armonk, New York, IBM is a significant cognitive service and integrated cloud solution firm that provides many technology and consulting solutions.
IBM is a leader in AI and Machine Learning technology verticals for 2021. This AI masters course will prepare students for Artificial Intelligence and Data Analytics careers.

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Add the IBM Advantage to your Learning
25 Industry-relevant Projects and Integrated labs
Immersive Learning Experience
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Python
Numpy
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