查看購物車
  • Simplified Chinese
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1516021

用於工業物聯網監控的嵌入式機器學習：技術的演變

Embedded ML for Industrial IoT Monitoring: Technology Evolution
出版日期: | 出版商: ABI Research | 英文 13 Pages | 商品交期: 最快1-2個工作天內

價格
簡介目錄

本報告提供用於工業物聯網監控的嵌入式機器學習趨勢調查，彙整嵌入式機器學習生態系統、主要供應商、關鍵組件、大規模部署嵌入式機器學習的障礙和解決方案以及條件為基礎的監測 (CBM) 的使用案例等資料。

實用的優點:

  • 了解工業物聯網 (IIoT) 中嵌入式機器學習 (ML) 的主要活動領域
  • 確定將解決方案推向市場所需的關鍵組件
  • 了解在受限邊緣環境中建置和部署模型的課題和發展機會

關鍵問題的答案：

  • 主要的嵌入式機器學習供應商有哪些？
  • 嵌入式機器學習開發人員面臨的主要問題是什麼？
  • 大規模部署嵌入式機器學習有哪些障礙以及如何克服這些障礙？

研究亮點：

  • 預測基於狀態的監控 (CBM) 用例中嵌入式機器學習的機會
  • 確定嵌入式機器學習技術供應商的主要趨勢和問題
  • 生態系圖顯示了嵌入式機器學習市場的關鍵組件和供應商

目錄

第一章主要發現

第二章主要預測

第三章主要公司與生態系

第 4 章 IIoT 嵌入式機器學習生態系

第 5 章 IIoT 中嵌入式機器學習的演進

  • 開發者工具集
  • 面向介紹的服務
簡介目錄
Product Code: AN-5894

Actionable Benefits:

  • Understand the key areas of activity for embedded Machine Learning (ML) in the Industrial Internet of Things (IIoT).
  • Identify the key components required to bring a solution to market.
  • Understand the challenges and development opportunities for building and deploying models in constrained edge environments.

Critical Questions Answered:

  • Who are some of the key vendors in embedded ML?
  • What are the key issues facing embedded ML developers?
  • What are the barriers to deploying embedded ML at scale, and how can these be overcome?

Research Highlights:

  • Forecasts on the addressable opportunity for deploying embedded ML in Condition-Based Monitoring (CBM) use cases.
  • Identification of key trends and discussion points among embedded ML technology suppliers.
  • Mapping the ecosystem to demonstrate the key components and vendors in the embedded ML market.

Who Should Read This?

  • Strategy and development teams at embedded ML companies looking to understand where they should focus on developing their products.
  • Software leaders at embedded hardware companies looking to understand how to build their ecosystem and ML product strategy.
  • Application providers and System Integrators (SIs) looking to understand the key discussion topics around embedded ML, and how they fit into the picture.

TABLE OF CONTENTS

1. KEY FINDINGS

2. KEY FORECASTS

3. KEY COMPANIES AND ECOSYSTEMS

4. EMBEDDED ML ECOSYSTEM FOR THE IIOT

5. EVOLUTION OF EMBEDDED ML IN THE IIOT

  • 5.1. DEVELOPER-FOCUSED TOOLSETS
  • 5.2. DEPLOYMENT-FOCUSED OFFERINGS