AI at the Edge: Solving Real-World Problems with Embedded Machine Learning (Paperback)

Edge artificial intelligence is transforming the way computers interact with the real world, allowing internet of things (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 flexible embedded Linux devices--for applications that reduce latency, protect privacy, and work without a network connection, greatly expanding the capabilities of the IoT.

This practical guide gives engineering professionals and product managers 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 roadmap will help you get started.

• Develop your expertise in artificial intelligence and machine learning on edge devices
• Understand which projects are best solved with edge AI
• Explore typical design patterns used with edge AI apps
• Use an iterative workflow to develop an edge AI application
• Optimize models for deployment to embedded devices
• Improve model performance based on feedback from real-world use

邊緣人工智慧正在改變電腦與現實世界互動的方式，讓物聯網（IoT）設備能夠利用之前因成本、頻寬或功耗限制而被丟棄的99%感測器數據做出決策。通過嵌入式機器學習等技術，開發人員可以捕捉人類直覺並將其部署到任何目標，從超低功耗微控制器到靈活的嵌入式Linux設備，用於降低延遲、保護隱私並在沒有網絡連接的情況下工作，大大擴展了物聯網的能力。

這本實用指南為工程專業人士和產品經理提供了一個端到端的框架，用於解決現實世界中的工業、商業和科學問題。您將探索整個過程的每個階段，從數據收集到模型優化、調整和測試，學習如何設計和支持邊緣人工智慧和嵌入式機器學習產品。邊緣人工智慧注定成為系統工程師的標準工具。這份高層次的路線圖將幫助您入門。

- 在邊緣設備上發展您的人工智慧和機器學習專業知識
- 理解哪些項目最適合使用邊緣人工智慧來解決
- 探索與邊緣人工智慧應用程序一起使用的典型設計模式
- 使用迭代工作流程開發邊緣人工智慧應用程序
- 將模型優化以部署到嵌入式設備
- 根據實際使用的反饋改善模型性能