エッジMLの実現

出版:ABI Research(ABIリサーチ) 出版年月:2022年5月

Edge ML Enablement: Development Platforms, Tools, and Solutions
エッジMLの実現:開発プラットフォーム、ツール、ソリューション
Research Analysis | 2Q 2022 | AN-4958

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種別 英文市場調査報告書(PDF)
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ABI Research(ABIリサーチ)「エッジMLの実現:開発プラットフォーム、ツール、ソリューション – Edge ML Enablement: Development Platforms, Tools, and Solutions」はエッジデバイスを活用した機械学習(ML)の実現について解説・分析したレポートです。

Actionable Benefits

  • Introduce readers to the edge ML enablement market.
  • Understand impactful technological and market trends that drive edge ML development.
  • Educate businesses on leveraging the skills of edge ML enablement vendors in their AI journey.

Critical Questions Answered

  • Who are the key edge ML enablement vendors and what do they offer?
  • Why are they important to the edge ML market?
  • What differentiates the different product offerings?

Research Highlights

  • Analysis of enabling technologies in edge ML enablement.
  • A detailed overview of key edge ML enablement vendors.
  • Market sizing of the edge ML enablement market.

Who Should Read This?

  • Edge ML implementers that are looking for external support.
  • Strategy planners and advisors within the edge AI industry.
  • System integrators helping businesses to implement edge ML applications.

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[プレスリリース]

Edge Machine Learning Enablement Market to Exceed US$5 Billion by 2027

New York, New York – June 09, 2022

In recent years, the industry has witnessed the migration of Machine Learning (ML) closer to the data source to create a better user experience and enhance privacy. To ease the challenges in design and development and accelerate adoption, many companies are offering development platforms, tools, libraries, and solutions for edge ML applications. As the adoption of these edge ML enablement platforms and solutions continue to grow, ABI Research, a global technology intelligence firm, is forecasting the edge ML enablement market to exceed US$5 billion by 2027.

Edge ML brings a significant upgrade in user experience, privacy protection, and cost optimization for end users. In addition, popular applications such as image recognition, predictive maintenance, and speech processing augment human experts with data-driven insights. However, to create a production-ready, enterprise-grade, edge ML solution, enterprises need to invest in large amounts of skill sets.

“To truly benefit from the benefits of edge ML applications, enterprises need to make sure their edge ML applications are accurate, optimized, and closely monitored to prevent biases and model drift. This means enterprises have to build a team of data scientists, device and embedded engineers, and project managers, which can become very expensive very quickly,” explains Lian Jye Su, Research Director at ABI Research. “Edge ML enablement vendors offer enterprises a hand in their Artificial Intelligence (AI) journey. They create user-friendly tools and libraries to lower the barrier of adoption.”

Specifically, edge ML enablement vendors enable enterprises to manage and govern their data for ML training and inference, select the suitable models, train and test the model, create an inference engine, and deploy and monitor the ML through edge ML Operations (MLOps). Many are offering application-specific solutions for dedicated applications such as audio classification, predictive maintenance, gesture recognition, and material detection. ABI Research has identified four major categories of edge ML enablement vendors, namely public cloud vendors, edge ML silicon vendors, platform-focused vendors, and technology-focused vendors.

Platform-focused vendors like Edge Impulse, Imagimob, and SensiML offer the most comprehensive databases, tools, libraries, and solutions for edge ML applications. Nonetheless, the rest of the field has unique value propositions. Technology-focused vendors like Latent AI and Plumerai focus on specific technology within the data science and ML stack. Edge ML silicon vendors like DeGirum and NXP offer developers highly optimized full-stack hardware and software solutions. Lastly, public cloud vendors allow enterprises to leverage the flexibility and scalable of cloud infrastructure when developing their edge ML applications.

“The market is still at a nascent stage. The majority of edge ML projects still fail to move past the experimental phase. Edge ML enablement vendors must continue to improve their low-code or no-code user experience for non-AI experts, expand edge ML hardware support, provide application-specific solutions and services, and ensure a high level of ML explainability to ensure legal compliance,” concludes Su.

These findings are from ABI Research’s Edge ML Enablement: Development Platforms, Tools, and Solutions application analysis report. This report is part of the company’s AI and Machine Learning research service, which includes research, data, and analyst insights. Based on extensive primary interviews, Application Analysis reports present in-depth analysis on key market trends and factors for a specific technology.

About ABI Research

ABI Research is a global technology intelligence firm delivering actionable research and strategic guidance to technology leaders, innovators, and decision makers around the world. Our research focuses on the transformative technologies that are dramatically reshaping industries, economies, and workforces today.


目次

Table of Contents

1. EXECUTIVE SUMMARY
2. KEY MARKET DRIVERS

2.1. Diverse Hardware Ecosystem
2.2. Compliance with Data Privacy Regulations
2.3. Lack of Big Data
2.4. Cost of Development

3. KEY TECHNOLOGIES

3.1. Data Management
3.2. Model Design
3.3. AutoML
3.4. Edge MLOps

4. VENDOR ECOSYSTEM

4.1. AWS
4.2. Blaize
4.3. DeGirum
4.4. Edge Impulse
4.5. Google and Qualcomm
4.6. Imagimob
4.7. Latent AI
4.8. MicroAI
4.9. Microsoft
4.10. Neuton
4.11. Nota
4.12. NXP
4.13. Plumerai
4.14. Qeexo
4.15. SensiML
4.16. STMicrolelectronics
4.17. Syntiant

5. MARKET FORECAST
6. KEY RECOMMENDATIONS

6.1. Recommendations for Vendors
6.2. Recommendations for ML Implementers and End Users

Companies Mentioned

Ambarella, Inc.
Apache Corporation
Aware, Inc.
AWS
Azure
Blaize
DeGirum
Edge Impluse
google
Imagimob
Intel Corporation
Interface, Inc.
Latent AI
MediaTek Inc
Microsoft
Microsoft Corporation
MircoAI
Monitor Group
Neuton
NN, Inc.
Nota
NXP
Open Technologies
Plumerai
Popular, Inc.
Processing Technologies
PT
Qeexo
Qualcomm
Qualcomm Inc
QuickLogic Corporation
SensiML
STMicroelectronics
STMicrolelectronics
Structured
Syntiant
Tucker Group
Xilinx, Inc.


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