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封面
市场调查报告书
商品编码
1516021

用于工业物联网监控的嵌入式机器学习：技术的演变

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个工作天内

价格
简介目录

本报告提供用于工业物联网监控的嵌入式机器学习趋势调查，彙整嵌入式机器学习生态系统、主要供应商、关键组件、大规模部署嵌入式机器学习的障碍和解决方案以及条件为基础的监测 (CBM) 的使用案例等资料。

实用的优点:

  • 了解工业物联网 (IIoT) 中嵌入式机器学习 (ML) 的主要活动领域
  • 确定将解决方案推向市场所需的关键组件
  • 了解在受限边缘环境中建置和部署模型的课题和发展机会

关键问题的答案：

  • 主要的嵌入式机器学习供应商有哪些？
  • 嵌入式机器学习开发人员面临的主要问题是什么？
  • 大规模部署嵌入式机器学习有哪些障碍以及如何克服这些障碍？

研究亮点：

  • 预测基于状态的监控 (CBM) 用例中嵌入式机器学习的机会
  • 确定嵌入式机器学习技术供应商的主要趋势和问题
  • 生态系图显示了嵌入式机器学习市场的关键组件和供应商

目录

第一章主要发现

第二章主要预测

第三章主要公司与生态系

第 4 章 IIoT 嵌入式机器学习生态系

第 5 章 IIoT 中嵌入式机器学习的演进

  • 开发者工具集
  • 面向介绍的服务
简介目录
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