AI&機械学習のリサーチサービス – 年間サービス


AI & Machine Learning Research Service

調査会社 ABIリサーチ
種別 英文年間契約型サービス
提供方法 オンライン(アクセス&ファイルダウンロード)


ABIリサーチの年間サービス「AI&機械学習のリサーチサービス – AI & Machine Learning Research Service」は人工知能(AI)と機械学習(マシンラーニング/ML)に関する市場情報を1年を通じて提供しています。

AI & Machine Learning Coverage

Our extensive coverage of AI and ML includes data, trends, forecasts, and benchmark and analysis reports. We assess the key technical and business factors that are essential for shaping AI and ML market activity and business models, including ML as a service, technology and platform as a service, software licensing, and edge AI hardware and applications. We also provide technology implementers with authoritative insight into the various AI and ML applications and use cases they should leverage to best streamline industrial and business processes as AI technology becomes accessible.

Our approach to market coverage is use-case centric as it looks at technology implementation for each use case studied. Aside from verticals that have existing AI implementation, such as consumer electronics and robotics, we also track AI and ML deployment in retail, manufacturing, energy, automotive, public safety and telecommunications. Special attention is dedicated to AI edge solutions.

Reports & Data ※タイトル毎でもご購入いただけます。各レポートの詳細はお問合せください。

タイトル 出版時期 形式
TinyML:最新市場情報 2022年第3四半期
TinyML: A Market Update
Research Report | 3Q 2022 | AN-5636
2022年9月 PDF
The Future of Technology in a Tumultuous World(無料レポート)
Whitepaper | 3Q 2022 | WP-1006
2022年9月 PDF
Edge ML Enablement Platform Vendors
Competitive Ranking | 3Q 2022 | CA-1341
2022年8月 PDF
人工知能と機械学習のマーケットデータ 2022年第2四半期
Artificial Intelligence and Machine Learning
Market Data | 2Q 2022 | MD-AIML-109
2022年6月 EXCEL
Edge ML Enablement: Development Platforms, Tools, and Solutions
Research Analysis | 2Q 2022 | AN-4958
2022年5月 PDF
連合型、分散型、FSL(Few-Shot learning):サーバーから機器まで
Federated, Distributed and Few-Shot Learning: From Servers to Devices
Research Analysis | 2Q 2022 | AN-4952
2022年4月 PDF
商用および産業用マシンビジョンのマーケットデータ 2021年第4四半期
Commercial and Industrial Machine Vision
Market Data | 4Q 2021 | MD-CIVM-101
2021年12月 EXCEL
Artificial Intelligence and Machine Learning
Market Data | 4Q 2021 | MD-AIML-108
2021年12月 マーケットデータ
Deep Learning-Based Sound Processing
Competitive Assessment | 4Q 2021 | CA-1323
2021年11月 分析レポート
Deep Learning-Based Ambient Sound and Language Processing: Cloud to Edge
Application Analysis Report | 3Q 2021 | AN-5031
2021年第3四半期 分析レポート
人工知能投資モニター 2020年
Artificial Intelligence Investment Monitor 2020
Application Analysis Report | 2Q 2021 | PT-2488
2021年第2四半期 分析レポート
Artificial Intelligence and Machine Learning
Data | 2Q 2021 | MD-AIML-107
2021年第2四半期 マーケットデータ
TinyML: The Next Big Opportunity in Tech
Report | 2Q 2021 | WP-WNGH-176
2021年第2四半期 無料レポート
The Edge AI Ecosystem
Application Analysis Report | 2Q 2021 | AN-5334
2021年第2四半期 分析レポート
Transformational Technology Summit: The Blossoming Of The Edge AI Ecosystem
Webinar Recording | 1Q 2021 | PT-2507
2021年第1四半期 ウェビナー
Deep Learning-Based Machine Vision in Smart Cities
Application Analysis Report | 1Q 2021 | AN-4938
2021年第1四半期 分析レポート
Artificial Intelligence and Machine Learning
Data | 1Q 2021 | MD-AIML-106
2021年第1四半期 マーケットデータ


Coverage areas include:

  • Machine learning
  • Artificial intelligence
  • Augmented Intelligence
  • Deep Learning
  • Data analytics
  • Predictive analytics
  • Prescriptive analytics
  • Algorithms and hardware technologies segmentation
  • Analysis of AI Tools and SDKs
  • AI and ML hot technology innovators
  • Edge AI and ML
  • Market segmentation and taxonomy of AI and ML use cases and applications
  • Different implementation approaches of AI and ML
  • AI and ML business models
  • AI and ML use cases in the telecoms industry
  • In-building systems including DAS (Distributed Antenna Systems)
  • RF power semiconductors for pulsed applications
  • Detailed spectrum analysis for 5G networks, sub-6G to mmWave, including regional disparities
  • AI and ML use cases in the manufacturing industry
  • AI and ML use cases in the consumer market
  • AI and ML use cases in the IoT market
  • The role of open source in shaping new applications and business models
  • Emerging trends in speech and image recognition, machine vision, natural language processing, touch/haptics, Generative and Creative Adversarial Networks, automated reasoning and security applications
  • Analysis of edge AI versus cloud AI

Answers To Today’s Critical AI & Machine Learning Questions

We help technology suppliers and implementers answer the most pressing questions about AI & Machine Learning, including:


Technology Suppliers:

  • How are the different ML hardware and algorithms are mapped against requirements of the different use cases addressed?
  • What are the key verticals that will drive AI and ML applications?
  • Should I create my own AI frameworks and solutions or should I adopt existing open frameworks?
  • Who are competitors I should watch and who are those I should partner with?
  • What emerging verticals should my organization target?
  • How big is the revenue opportunity?
  • What major challenges will the industry face when managing a myriad of data generated by billions of connected devices?
  • Who are the companies and organizations my company should partner with to create adequate solutions for the verticals are targeting?
  • Where does my company fit in the AI/ML competitive landscape?
  • How can my organization productize open source code?
  • How can we stream value from it?
  • What are the most successful open-source communities and frameworks for my company to rely on?
  • Do I have any benefit from contributing and using open source and what are the risks?
    What are the most invested in AI R&D projects and frameworks?
    What impact will the move from cloud-based to edge based have on the market dynamics and supplier positioning?

Implementers & End Users:

  • How can I implement AI in my current business activities?
    How will AI create new market opportunities in my sector?
    What is the realistic time to maturity of different AI components?
    What is the best approach for integrating AI into my company’s ecosystem?
    What criteria should I consider when choosing an AI partner?
    What advanced analytics techniques should my company consider adopting?
    What are the main types of algorithms used in ML today and how this is going to evolve in the future?
    How can my company utilize AI to simplify our business and operation processes?
    What is the difference between predictive and prescriptive analytics, and what is the best course of action for my company to take to effectively keep tabs on all our generated data?
    What can my company discern from our generated data through advanced analytics?
    Are there any security concerns my company should be made aware of when relying on advanced analytics?
    How can my company protect our data and our customers’ data? • What is the value of edge computing versus cloud computing?
    Should I be using an open-source AI framework to develop models, and which one would suit my needs?



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