基于状态的监测 (CBM) 的嵌入式 ML 趋势和策略：生态系统、路线图和实施

本报告提供基于状态的监测 (CBM) 转动内建式ML的市场调查，提供内建式ML的价值链，各种CBM市场上内建式ML的引进预测，内建式ML供应商的案例研究，需求方面的必要条件的分析等资讯。

实用的优点:

• 了解 IIoT 供应商的嵌入式机器学习 (ML) 策略和路线图
• 为您的市场推广策略确定目标合作伙伴
• 从 OEM 和感测器製造商的角度深入了解需求方的需求

重要问题的答案：

• 基于状态监测 (CBM) 应用的嵌入式机器学习的市场进入策略如何演变？
• 哪些嵌入式机器学习供应商和合作伙伴提供最成熟的产品？
• IIoT 价值链中每个供应商的优先事项是什么？

研究亮点：

• 预测嵌入式机器学习在各 CBM 市场的引入
• 多供应商类型案例研究：使用嵌入式机器学习演示活动
• 分析生态系中各参与者的优先事项，以及他们希望如何与合作伙伴合作，将嵌入式机器学习带给客户。

目录

主要发现

主要预测

主要公司

介绍趋势

CBM 感测器用例：云主导 AIML

边缘迁移到 AIML：CBM 应用迁移缓慢

嵌入式机器学习价值链

供应商视角：应用平台

供应商的观点：边缘模型开发工具

供应商视角：网路与自动化设备供应商

实施公司视角：OEM

介绍公司观点：改造感测器

实施公司的观点：营运经理

Actionable Benefits:

• Understand Industrial Internet of Things (IIoT) vendors’ strategies and roadmaps for embedded Machine Learning (ML).
• Identify target partners for go-to-market strategies.
• Gain insight into demand-side requirements, with the perspectives of Original Equipment Manufacturers (OEMs) and sensor manufacturers.

Critical Questions Answered:

• How are go-to-market strategies evolving for embedded ML for Condition-Based Monitoring (CBM) applications?
• Which embedded ML suppliers and partners are most advanced in their offering maturity?
• What are the priorities of different suppliers across the IIoT value chain, and how should they interact to bring embedded ML products to customers?

Research Highlights:

• Forecasts on embedded ML adoption in different CBM markets.
• Case studies of multiple supplier types to demonstrate their activities in using embedded ML.
• Analysis of different ecosystem participants’ priorities and how they want to work with partners to bring embedded ML to customers.

Who Should Read This?

• Strategy and development teams at embedded ML companies looking to understand how they should bring their products to market in the industrial space.
• Product and strategy teams at IIoT software companies looking to understand how to incorporate embedded ML offerings into their marketplaces.
• Application providers and system integrators looking to understand the key discussion topics around embedded ML, and how they fit into the picture.

TABLE OF CONTENTS

Key Findings

Key Forecasts

Key Companies

Adoption Trends

Cloud Dominates AIML for CBM Sensor Use Cases

Shift Towards AIML at the Edge - CBM Applications Slower to Move

Embedded ML Value Chain

Supplier Perspective Application Platforms

Supplier Perspective Edge Model Development Tools

Supplier Perspective Networking and Automation Equipment Vendors

Adopter Perspective Original Equipment Manufacturers OEMs

Adopter Perspective Retrofit Sensors

Adopter Perspective Operation Managers