查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1593919

機器學習即服務市場：按組件、應用程式和最終用戶分類 - 2025-2030 年全球預測

Machine-Learning-as-a-Service Market by Component (Services, Software), Application (Augmented & Virtual Reality, Fraud Detection & Risk Management, Marketing & Advertising), End User - Global Forecast 2025-2030
出版日期: | 出版商: 360iResearch | 英文 198 Pages | 商品交期: 最快1-2個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

2023 年機器學習即服務市場價值為 214.8 億美元，預計到 2024 年將達到 280 億美元，複合年成長率為 30.40%，到 2030 年將達到 1377 億美元。

機器學習即服務 (MLaaS) 是雲端基礎的服務，可為企業提供全面的機器學習工具、技術和應用程式，無需深厚的資料科學專業知識或大型基礎設施投資平台。該服務對於實現高級分析的民主化以及使各行業能夠利用高級演算法進行巨量資料分析、預測分析和複雜的決策流程至關重要。應用涵蓋醫療保健、金融、零售、製造等領域，並提供詐欺偵測、個人化行銷、客戶洞察和業務效率等功能。最終用途範圍還包括希望將人工智慧無縫整合到其工作流程中並加快創新產品和服務的上市時間的公司。

主要市場統計
基準年[2023] 214.8億美元
預測年份 [2024] 280億美元
預測年份 [2030] 1377.8億美元
複合年成長率（%） 30.40%

MLaaS 市場的關鍵成長要素包括資料激增、推動雲端採用以及對人工智慧主導解決方案不斷成長的需求。企業希望透過資料主導的洞察來獲得競爭優勢，這推動了對 MLaaS 平台的需求。特別是，挑戰在於開發針對特定行業挑戰的利基解決方案、提高模型可解釋性以及加強隱私保護。企業可以透過投資強大的網路安全措施和擴大多語言支援以進入新興市場而受益。

阻礙成長的挑戰包括資料隱私問題、監管挑戰以及缺乏熟練的專業人員來解釋複雜的產出。此外，MLaaS 解決方案通常面臨與現有基礎設施整合的挑戰。為了克服這些問題，公司應該透過與 IT 顧問公司合作，專注於開發具有更簡單整合機制的使用者友善平台。

可以透過探索自動化機器學習 (AutoML)、邊緣運算整合和提高模型透明度來促進創新，以促進信任建立和監管合規性。此外，促進學術界和工業界之間的合作可能會產生適合特定應用的新穎演算法。隨著技術的快速進步和消費者需求模式的不斷變化，市場的本質仍然是動態的。透過策略性地應對這些因素並優先考慮持續學習和適應性，公司可以最大限度地發揮 MLaaS 的潛力，並在這個快速成長的市場中站穩腳跟。

市場動態：揭示快速發展的機器學習即服務市場的關鍵市場洞察

供給和需求的動態交互作用正在改變機器學習即服務市場。透過了解這些不斷變化的市場動態，公司可以準備好做出明智的投資決策、完善策略決策並抓住新的商機。全面了解這些趨勢可以幫助企業降低政治、地理、技術、社會和經濟領域的風險，同時也能幫助企業了解消費行為及其對製造業的影響。

  • 市場促進因素
    • 物聯網和自動化的採用增加
    • 擴大雲端基礎的服務的使用
    • 多個行業需要提高績效和業務效率
  • 市場限制因素
    • 缺乏訓練有素的專業人員
  • 市場機會
    • 透過認知運算、神經網路、深度學習技術和人工智慧 (AI) 的整合實現技術進步
    • 擴大醫療健康產業投資與合作
  • 市場挑戰
    • 資料安全和隱私問題

波特五力：駕馭機器學習即服務市場的策略工具

波特的五力框架是理解市場競爭格局的重要工具。波特的五力框架為評估公司的競爭地位和探索策略機會提供了清晰的方法。該框架可幫助公司評估市場動態並確定新業務的盈利。這些見解使公司能夠利用自己的優勢，解決弱點並避免潛在的挑戰，從而確保更強大的市場地位。

PESTLE分析：了解機器學習即服務市場的外部影響

外部宏觀環境因素在塑造機器學習即服務市場的績效動態方面發揮著至關重要的作用。對政治、經濟、社會、技術、法律和環境因素的分析提供了應對這些影響所需的資訊。透過調查 PESTLE 因素，公司可以更了解潛在的風險和機會。這種分析可以幫助公司預測法規、消費者偏好和經濟趨勢的變化，並幫助他們做出積極主動的決策。

市場佔有率分析 了解機器學習即服務市場的競爭格局

機器學習即服務市場的詳細市場佔有率分析可以對供應商績效進行全面評估。公司可以透過比較收益、客戶群和成長率等關鍵指標來揭示其競爭地位。該分析揭示了市場集中、分散和整合的趨勢，為供應商提供了製定策略決策所需的洞察力，使他們能夠在日益激烈的競爭中佔有一席之地。

FPNV 機器學習即服務市場供應商的定位矩陣績效評估

FPNV 定位矩陣是評估機器學習即服務市場供應商的重要工具。此矩陣允許業務組織根據供應商的商務策略和產品滿意度評估供應商，從而做出符合其目標的明智決策。這四個象限使您能夠清晰、準確地分類供應商，並確定最能滿足您的策略目標的合作夥伴和解決方案。

策略分析與建議 規劃您在機器學習即服務市場的成功之路

對於旨在加強其在全球市場的影響力的公司來說，對機器學習即服務市場的策略分析至關重要。透過審查關鍵資源、能力和績效指標，公司可以識別成長機會並努力改進。這種方法使您能夠克服競爭環境中的挑戰，利用新的商機，並取得長期成功。

本報告對市場進行了全面分析，涵蓋關鍵重點領域：

1. 市場滲透率：對當前市場環境的詳細審查、主要企業的廣泛資料、對其在市場中的影響力和整體影響力的評估。

2. 市場開拓：辨識新興市場的成長機會，評估現有領域的擴張潛力，並提供未來成長的策略藍圖。

3. 市場多元化：分析近期產品發布、開拓地區、關鍵產業進展、塑造市場的策略投資。

4. 競爭評估與情報：徹底分析競爭格局，檢驗市場佔有率、業務策略、產品系列、認證、監理核准、專利趨勢、主要企業的技術進步等。

5. 產品開發與創新：重點在於有望推動未來市場成長的最尖端科技、研發活動和產品創新。

我們也回答重要問題，以幫助相關人員做出明智的決策：

1.目前的市場規模和未來的成長預測是多少？

2. 哪些產品、區隔市場和地區提供最佳投資機會？

3.塑造市場的主要技術趨勢和監管影響是什麼？

4.主要廠商的市場佔有率和競爭地位如何？

5. 推動供應商市場進入和退出策略的收益來源和策略機會是什麼？

目錄

第1章 前言

第2章調查方法

第3章執行摘要

第4章市場概況

第5章市場洞察

  • 市場動態
    • 促進因素
      • 物聯網和自動化的採用增加
      • 增加雲端基礎的服務的使用
      • 需要提高跨產業的績效和營運效率
    • 抑制因素
      • 缺乏訓練有素的專業人員
    • 機會
      • 整合認知運算、神經網路、深度學習技術和人工智慧 (AI) 的技術進步
      • 擴大醫療健康產業投資合作
    • 任務
      • 資料安全和隱私問題
  • 市場區隔分析
  • 波特五力分析
  • PESTEL分析
    • 政治的
    • 經濟
    • 社群
    • 技術的
    • 合法地
    • 環境

第6章 機器學習即服務市場：依組成部分

  • 服務
  • 軟體

第7章 機器學習即服務市場：依應用分類

  • 擴增實境和虛擬實境
  • 詐騙偵測和風險管理
  • 行銷和廣告
  • 預測分析
  • 安全和監視

第 8 章 機器學習即服務市場：依最終使用者分類

  • BFSI
  • 醫療保健和生命科學
  • 製造業
  • 零售
  • 通訊

第 9 章 美洲機器學習即服務市場

  • 阿根廷
  • 巴西
  • 加拿大
  • 墨西哥
  • 美國

第10章亞太地區機器學習即服務市場

  • 澳洲
  • 中國
  • 印度
  • 印尼
  • 日本
  • 馬來西亞
  • 菲律賓
  • 新加坡
  • 韓國
  • 台灣
  • 泰國
  • 越南

第11章歐洲、中東和非洲的機器學習即服務市場

  • 丹麥
  • 埃及
  • 芬蘭
  • 法國
  • 德國
  • 以色列
  • 義大利
  • 荷蘭
  • 奈及利亞
  • 挪威
  • 波蘭
  • 卡達
  • 俄羅斯
  • 沙烏地阿拉伯
  • 南非
  • 西班牙
  • 瑞典
  • 瑞士
  • 土耳其
  • 阿拉伯聯合大公國
  • 英國

第12章競爭格局

  • 2023 年市場佔有率分析
  • FPNV 定位矩陣，2023
  • 競爭情境分析
  • 戰略分析和建議

公司名單

  • Amazon.com Inc.
  • AT&T Inc.
  • BigML, Inc.
  • Fair Isaac Corporation
  • Google LLC
  • H2O.ai
  • Hewlett Packard Enterprise Company
  • IBM Corp.
  • Iflowsoft Solutions Inc.
  • Microsoft Corporation
  • Monkeylearn Inc.
  • SAS Institute Inc.
  • Sift Science Inc.
  • Yottamine Analytics, LLC
Product Code: MRR-43286DA08063

The Machine-Learning-as-a-Service Market was valued at USD 21.48 billion in 2023, expected to reach USD 28.00 billion in 2024, and is projected to grow at a CAGR of 30.40%, to USD 137.78 billion by 2030.

Machine-Learning-as-a-Service (MLaaS) refers to a cloud-based platform offering comprehensive machine learning tools, techniques, and applications for businesses without requiring in-depth expertise in data science or extensive infrastructure investment. This service is essential for democratizing access to advanced analytics, enabling various industries to leverage sophisticated algorithms for big data analysis, predictive analytics, and complex decision-making processes. Its application spans across sectors such as healthcare, finance, retail, and manufacturing, facilitating functions like fraud detection, personalized marketing, customer insights, and operational efficiency enhancement. The end-use scope includes companies seeking to integrate AI into their workflow seamlessly, reducing time-to-market for innovative products and services.

KEY MARKET STATISTICS
Base Year [2023] USD 21.48 billion
Estimated Year [2024] USD 28.00 billion
Forecast Year [2030] USD 137.78 billion
CAGR (%) 30.40%

Key growth factors for the MLaaS market include increasing data proliferation, a push towards cloud adoption, and rising demand for AI-driven solutions. Organizations are striving for competitive advantages through data-driven insights, which is propelling demand for MLaaS platforms. Opportunities exist particularly in developing niche solutions tailored to industry-specific challenges, improving model explainability, and enhancing privacy protections. Companies can benefit by investing in robust cybersecurity measures and expanding multi-language support to capture emerging markets.

Limitations hindering growth include concerns over data privacy, regulatory challenges, and a shortage of skilled professionals to interpret complex outputs. Additionally, MLaaS solutions often face integration challenges with existing infrastructure. To overcome these, companies should focus on developing user-friendly platforms with easier integration mechanisms, possibly through partnerships with IT consultancies.

Innovation can be spurred through research in automated machine learning (AutoML), edge computing integration, and enhanced model transparency which can build trust and ease regulatory compliance. Moreover, fostering collaborations between academia and industry could yield novel algorithms suited for specific applications. The nature of the market remains dynamic, with rapid technological advancements and shifts in consumer demand patterns. By strategically navigating these factors and prioritizing continual learning and adaptability, businesses can harness MLaaS's full potential and secure their foothold in this burgeoning market.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Machine-Learning-as-a-Service Market

The Machine-Learning-as-a-Service Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

  • Market Drivers
    • Rising adoption of IoT and automation
    • Growing usage of cloud-based services
    • Need to improve performance and operational efficiency in the several industry
  • Market Restraints
    • Lack of trained professionals
  • Market Opportunities
    • Advancements in technologies with the integration of cognitive computing, neural networks, deep learning technologies, and artificial intelligence (AI)
    • Growing investments and collaboration in the healthcare Industry
  • Market Challenges
    • Data security and privacy concerns

Porter's Five Forces: A Strategic Tool for Navigating the Machine-Learning-as-a-Service Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Machine-Learning-as-a-Service Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Machine-Learning-as-a-Service Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Machine-Learning-as-a-Service Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Machine-Learning-as-a-Service Market

A detailed market share analysis in the Machine-Learning-as-a-Service Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Machine-Learning-as-a-Service Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Machine-Learning-as-a-Service Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Machine-Learning-as-a-Service Market

A strategic analysis of the Machine-Learning-as-a-Service Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Machine-Learning-as-a-Service Market, highlighting leading vendors and their innovative profiles. These include Amazon.com Inc., AT&T Inc., BigML, Inc., Fair Isaac Corporation, Google LLC, H2O.ai, Hewlett Packard Enterprise Company, IBM Corp., Iflowsoft Solutions Inc., Microsoft Corporation, Monkeylearn Inc., SAS Institute Inc., Sift Science Inc., and Yottamine Analytics, LLC.

Market Segmentation & Coverage

This research report categorizes the Machine-Learning-as-a-Service Market to forecast the revenues and analyze trends in each of the following sub-markets:

  • Based on Component, market is studied across Services and Software.
  • Based on Application, market is studied across Augmented & Virtual Reality, Fraud Detection & Risk Management, Marketing & Advertising, Predictive Analytics, and Security & Surveillance.
  • Based on End User, market is studied across BFSI, Healthcare & Life Sciences, Manufacturing, Retail, and Telecom.
  • Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Segmentation & Coverage
  • 1.3. Years Considered for the Study
  • 1.4. Currency & Pricing
  • 1.5. Language
  • 1.6. Stakeholders

2. Research Methodology

  • 2.1. Define: Research Objective
  • 2.2. Determine: Research Design
  • 2.3. Prepare: Research Instrument
  • 2.4. Collect: Data Source
  • 2.5. Analyze: Data Interpretation
  • 2.6. Formulate: Data Verification
  • 2.7. Publish: Research Report
  • 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

  • 5.1. Market Dynamics
    • 5.1.1. Drivers
      • 5.1.1.1. Rising adoption of IoT and automation
      • 5.1.1.2. Growing usage of cloud-based services
      • 5.1.1.3. Need to improve performance and operational efficiency in the several industry
    • 5.1.2. Restraints
      • 5.1.2.1. Lack of trained professionals
    • 5.1.3. Opportunities
      • 5.1.3.1. Advancements in technologies with the integration of cognitive computing, neural networks, deep learning technologies, and artificial intelligence (AI)
      • 5.1.3.2. Growing investments and collaboration in the healthcare Industry
    • 5.1.4. Challenges
      • 5.1.4.1. Data security and privacy concerns
  • 5.2. Market Segmentation Analysis
  • 5.3. Porter's Five Forces Analysis
    • 5.3.1. Threat of New Entrants
    • 5.3.2. Threat of Substitutes
    • 5.3.3. Bargaining Power of Customers
    • 5.3.4. Bargaining Power of Suppliers
    • 5.3.5. Industry Rivalry
  • 5.4. PESTLE Analysis
    • 5.4.1. Political
    • 5.4.2. Economic
    • 5.4.3. Social
    • 5.4.4. Technological
    • 5.4.5. Legal
    • 5.4.6. Environmental

6. Machine-Learning-as-a-Service Market, by Component

  • 6.1. Introduction
  • 6.2. Services
  • 6.3. Software

7. Machine-Learning-as-a-Service Market, by Application

  • 7.1. Introduction
  • 7.2. Augmented & Virtual Reality
  • 7.3. Fraud Detection & Risk Management
  • 7.4. Marketing & Advertising
  • 7.5. Predictive Analytics
  • 7.6. Security & Surveillance

8. Machine-Learning-as-a-Service Market, by End User

  • 8.1. Introduction
  • 8.2. BFSI
  • 8.3. Healthcare & Life Sciences
  • 8.4. Manufacturing
  • 8.5. Retail
  • 8.6. Telecom

9. Americas Machine-Learning-as-a-Service Market

  • 9.1. Introduction
  • 9.2. Argentina
  • 9.3. Brazil
  • 9.4. Canada
  • 9.5. Mexico
  • 9.6. United States

10. Asia-Pacific Machine-Learning-as-a-Service Market

  • 10.1. Introduction
  • 10.2. Australia
  • 10.3. China
  • 10.4. India
  • 10.5. Indonesia
  • 10.6. Japan
  • 10.7. Malaysia
  • 10.8. Philippines
  • 10.9. Singapore
  • 10.10. South Korea
  • 10.11. Taiwan
  • 10.12. Thailand
  • 10.13. Vietnam

11. Europe, Middle East & Africa Machine-Learning-as-a-Service Market

  • 11.1. Introduction
  • 11.2. Denmark
  • 11.3. Egypt
  • 11.4. Finland
  • 11.5. France
  • 11.6. Germany
  • 11.7. Israel
  • 11.8. Italy
  • 11.9. Netherlands
  • 11.10. Nigeria
  • 11.11. Norway
  • 11.12. Poland
  • 11.13. Qatar
  • 11.14. Russia
  • 11.15. Saudi Arabia
  • 11.16. South Africa
  • 11.17. Spain
  • 11.18. Sweden
  • 11.19. Switzerland
  • 11.20. Turkey
  • 11.21. United Arab Emirates
  • 11.22. United Kingdom

12. Competitive Landscape

  • 12.1. Market Share Analysis, 2023
  • 12.2. FPNV Positioning Matrix, 2023
  • 12.3. Competitive Scenario Analysis
  • 12.4. Strategy Analysis & Recommendation

Companies Mentioned

  • 1. Amazon.com Inc.
  • 2. AT&T Inc.
  • 3. BigML, Inc.
  • 4. Fair Isaac Corporation
  • 5. Google LLC
  • 6. H2O.ai
  • 7. Hewlett Packard Enterprise Company
  • 8. IBM Corp.
  • 9. Iflowsoft Solutions Inc.
  • 10. Microsoft Corporation
  • 11. Monkeylearn Inc.
  • 12. SAS Institute Inc.
  • 13. Sift Science Inc.
  • 14. Yottamine Analytics, LLC

LIST OF FIGURES

  • FIGURE 1. MACHINE-LEARNING-AS-A-SERVICE MARKET RESEARCH PROCESS
  • FIGURE 2. MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2023 VS 2030
  • FIGURE 3. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2018-2030 (USD MILLION)
  • FIGURE 4. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 5. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 6. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2023 VS 2030 (%)
  • FIGURE 7. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 8. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2023 VS 2030 (%)
  • FIGURE 9. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 10. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2023 VS 2030 (%)
  • FIGURE 11. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 12. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 13. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 14. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY STATE, 2023 VS 2030 (%)
  • FIGURE 15. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 16. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 17. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 18. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 19. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 20. MACHINE-LEARNING-AS-A-SERVICE MARKET SHARE, BY KEY PLAYER, 2023
  • FIGURE 21. MACHINE-LEARNING-AS-A-SERVICE MARKET, FPNV POSITIONING MATRIX, 2023

LIST OF TABLES

  • TABLE 1. MACHINE-LEARNING-AS-A-SERVICE MARKET SEGMENTATION & COVERAGE
  • TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2023
  • TABLE 3. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2018-2030 (USD MILLION)
  • TABLE 4. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 5. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 6. MACHINE-LEARNING-AS-A-SERVICE MARKET DYNAMICS
  • TABLE 7. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 8. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 9. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SOFTWARE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 10. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 11. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AUGMENTED & VIRTUAL REALITY, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 12. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY FRAUD DETECTION & RISK MANAGEMENT, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 13. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MARKETING & ADVERTISING, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 14. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 15. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SECURITY & SURVEILLANCE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 16. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 17. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 18. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE & LIFE SCIENCES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 19. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 20. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 21. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY TELECOM, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 22. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 23. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 24. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 25. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 26. ARGENTINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 27. ARGENTINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 28. ARGENTINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 29. BRAZIL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 30. BRAZIL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 31. BRAZIL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 32. CANADA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 33. CANADA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 34. CANADA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 35. MEXICO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 36. MEXICO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 37. MEXICO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 38. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 39. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 40. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 41. UNITED STATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
  • TABLE 42. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 43. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 44. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 45. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 46. AUSTRALIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 47. AUSTRALIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 48. AUSTRALIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 49. CHINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 50. CHINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 51. CHINA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 52. INDIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 53. INDIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 54. INDIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 55. INDONESIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 56. INDONESIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 57. INDONESIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 58. JAPAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 59. JAPAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 60. JAPAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 61. MALAYSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 62. MALAYSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 63. MALAYSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 64. PHILIPPINES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 65. PHILIPPINES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 66. PHILIPPINES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 67. SINGAPORE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 68. SINGAPORE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 69. SINGAPORE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 70. SOUTH KOREA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 71. SOUTH KOREA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 72. SOUTH KOREA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 73. TAIWAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 74. TAIWAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 75. TAIWAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 76. THAILAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 77. THAILAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 78. THAILAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 79. VIETNAM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 80. VIETNAM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 81. VIETNAM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 82. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 83. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 84. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 85. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 86. DENMARK MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 87. DENMARK MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 88. DENMARK MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 89. EGYPT MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 90. EGYPT MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 91. EGYPT MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 92. FINLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 93. FINLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 94. FINLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 95. FRANCE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 96. FRANCE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 97. FRANCE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 98. GERMANY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 99. GERMANY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 100. GERMANY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 101. ISRAEL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 102. ISRAEL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 103. ISRAEL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 104. ITALY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 105. ITALY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 106. ITALY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 107. NETHERLANDS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 108. NETHERLANDS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 109. NETHERLANDS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 110. NIGERIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 111. NIGERIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 112. NIGERIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 113. NORWAY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 114. NORWAY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 115. NORWAY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 116. POLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 117. POLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 118. POLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 119. QATAR MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 120. QATAR MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 121. QATAR MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 122. RUSSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 123. RUSSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 124. RUSSIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 125. SAUDI ARABIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 126. SAUDI ARABIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 127. SAUDI ARABIA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 128. SOUTH AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 129. SOUTH AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 130. SOUTH AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 131. SPAIN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 132. SPAIN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 133. SPAIN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 134. SWEDEN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 135. SWEDEN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 136. SWEDEN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 137. SWITZERLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 138. SWITZERLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 139. SWITZERLAND MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 140. TURKEY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 141. TURKEY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 142. TURKEY MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 143. UNITED ARAB EMIRATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 144. UNITED ARAB EMIRATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 145. UNITED ARAB EMIRATES MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 146. UNITED KINGDOM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 147. UNITED KINGDOM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION, 2018-2030 (USD MILLION)
  • TABLE 148. UNITED KINGDOM MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY END USER, 2018-2030 (USD MILLION)
  • TABLE 149. MACHINE-LEARNING-AS-A-SERVICE MARKET SHARE, BY KEY PLAYER, 2023
  • TABLE 150. MACHINE-LEARNING-AS-A-SERVICE MARKET, FPNV POSITIONING MATRIX, 2023