AI at the edge : solving real-world problems with embedded machine learning / Daniel Situnayake and Jenny Plunkett.
Material type:
TextPublisher: Sebastopol, CA ; O'Reilly, 2023Edition: First editionDescription: xxiv, 487 pages : illustrations ; 24 cmContent type: - text
- unmediated
- volume
- 9789355422323
- Machine learning
- Embedded computer systems
- Internet of things
- COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition
- COMPUTERS / Computer Vision & Pattern Recognition
- COMPUTERS / Data Science / Machine Learning
- COMPUTERS / Embedded Computer Systems
- COMPUTERS / Image Processing
- COMPUTERS / Internet of Things (IoT) *
- COMPUTERS / Natural Language Processing
- COMPUTERS / Neural Networks
- COMPUTERS / Speech & Audio Processing
- Embedded computer systems
- Internet of things
- Machine learning
- 006.31 SIT 23rd
- Q325.5 .S58 2023
| Item type | Current library | Call number | Status | Notes | Date due | Barcode | |
|---|---|---|---|---|---|---|---|
| Text Book | VIT-AP | 006.31 SIT (Browse shelf(Opens below)) | In transit from VIT-AP to School of Computer Science Section since 2025-04-09 | CSE | 022483 | ||
| Reference Book | VIT-AP Reference | 006.31 SIT (Browse shelf(Opens below)) | Not For Loan | CSE | 022482 |
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
Includes bibliographical references and index.
1. A Brief Introduction to Edge Al -- 2. Edge AI in the Real World -- 3. The Hardware of Edge AI -- 4. Algorithms for Edge AI -- 5. Tools and Expertise -- 6. Understanding and Framing Problems -- 7. How to Build a Dataset -- 8. Designing Edge AI Applications -- 9. Developing Edge AI Applications -- 10. Evaluating, Deploying, and Supporting Edge AI Applications -- 11. Use Case : Wildlife Monitoring -- 12. Use Case : Food Quality Assurance -- 13. Use Case : Consumer Products
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. -- Provided by publisher.
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