基於狀態的監測 (CBM) 的嵌入式 ML 趨勢和策略：生態系統、路線圖和實施

本報告提供基於狀態的監測 (CBM) 轉動內建式ML的市場調查，提供內建式ML的價值鏈，各種CBM市場上內建式ML的引進預測，內建式ML供應商的案例研究，需求方面的必要條件的分析等資訊。

實用的優點:

• 了解 IIoT 供應商的嵌入式機器學習 (ML) 策略和路線圖
• 為您的市場推廣策略確定目標合作夥伴
• 從 OEM 和感測器製造商的角度深入了解需求方的需求

重要問題的答案：

• 基於狀態監測 (CBM) 應用的嵌入式機器學習的市場進入策略如何演變？
• 哪些嵌入式機器學習供應商和合作夥伴提供最成熟的產品？
• IIoT 價值鏈中每個供應商的優先事項是什麼？

研究亮點：

• 預測嵌入式機器學習在各 CBM 市場的引入
• 多供應商類型案例研究：使用嵌入式機器學習演示活動
• 分析生態系中各參與者的優先事項，以及他們希望如何與合作夥伴合作，將嵌入式機器學習帶給客戶。

目錄

主要發現

主要預測

主要公司

介紹趨勢

CBM 感測器用例：雲主導 AIML

邊緣遷移到 AIML：CBM 應用遷移緩慢

嵌入式機器學習價值鏈

供應商視角：應用平台

供應商的觀點：邊緣模型開發工具

供應商視角：網路與自動化設備供應商

實施公司視角：OEM

介紹公司觀點：改造感測器

實施公司的觀點：營運經理

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