AI at the Edge Solving Real-World Problems with Embedded Machine Learning

[Farid]

AI at the Edge | 513 | Daniel Situnayake;Jenny Plunkett; |

Edge AI is transforming the way computers interact with the real world, allowing IoT devices to make decisions using the 99% of sensor data that was previously discarded due to cost, bandwidth, or power limitations. With techniques like embedded machine learning, developers can capture human intuition and deploy it to any target-from ultra-low power microcontrollers to embedded Linux devices.

This practical guide gives engineering professionals, including product managers and technology leaders, an end-to-end framework for solving real-world industrial, commercial, and scientific problems with edge AI. You'll explore every stage of the process, from data collection to model optimization to tuning and testing, as you learn how to design and support edge AI and embedded ML products. Edge AI is destined to become a standard tool for systems engineers. This high-level road map helps you get started.

Develop your expertise in AI and ML for edge devices
Understand which projects are best solved with edge AI
Explore key design patterns for edge AI apps
Learn an iterative workflow for developing AI systems
Build a team with the skills to solve real-world problems
Follow a responsible AI process to create effective products



Contents of Download:
AI at the Edge Solving Real-World Problems with Embedded Machine Learning.pdf (165.94 MB)


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