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市场调查报告书
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1593919

机器学习即服务市场：按组件、应用程式和最终用户划分 - 2025-2030 年全球预测

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个工作天内

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2023 年机器学习即服务市场价值为 214.8 亿美元，预计到 2024 年将达到 280 亿美元，复合年增长率为 30.40%，到 2030 年将达到 1377 亿美元。

机器学习即服务 (MLaaS) 是云端基础的服务，可为企业提供全面的机器学习工具、技术和应用程序，无需深厚的资料科学专业知识或大型基础设施投资平台。该服务对于实现高级分析的民主化以及使各行业能够利用高级演算法进行巨量资料分析、预测分析和复杂的决策流程至关重要。应用涵盖医疗保健、金融、零售、製造等领域，并提供诈欺侦测、个人化行销、客户洞察和业务效率等功能。最终用途范围还包括希望将人工智慧无缝整合到其工作流程中并加快创新产品和服务的上市时间的公司。

主要市场统计
基准年[2023] 214.8亿美元
预测年份 [2024] 280亿美元
预测年份 [2030] 1377.8亿美元
复合年增长率（%） 30.40%

MLaaS 市场的关键成长要素包括资料激增、推动云端采用以及对人工智慧主导解决方案不断增长的需求。企业希望透过资料主导的洞察来获得竞争优势，这推动了对 MLaaS 平台的需求。特别是，挑战在于开发针对特定行业挑战的利基解决方案、提高模型可解释性以及加强隐私保护。企业可以透过投资强大的网路安全措施和扩大多语言支援以进入新兴市场而受益。

阻碍成长的挑战包括资料隐私问题、监管挑战以及缺乏熟练的专业人员来解释复杂的产出。此外，MLaaS 解决方案通常面临与现有基础设施整合的挑战。为了克服这些问题，公司应该透过与 IT 顾问公司合作，专注于开发具有更简单整合机制的使用者友善平台。

可以透过探索自动化机器学习 (AutoML)、边缘运算整合和提高模型透明度来促进创新，以促进信任建立和监管合规性。此外，促进学术界和工业界之间的合作可能会产生适合特定应用的新颖演算法。随着技术的快速进步和消费者需求模式的不断变化，市场的本质仍然是动态的。透过策略性地应对这些因素并优先考虑持续学习和适应性，公司可以最大限度地发挥 MLaaS 的潜力，并在这个快速成长的市场中站稳脚跟。

市场动态：揭示快速发展的机器学习即服务市场的关键市场洞察

供给和需求的动态交互作用正在改变机器学习即服务市场。透过了解这些不断变化的市场动态，公司可以准备好做出明智的投资决策、完善策略决策并抓住新的商机。全面了解这些趋势可以帮助企业降低政治、地理、技术、社会和经济领域的风险，同时也能帮助企业了解消费行为及其对製造业的影响。

  • 市场驱动因素
    • 物联网和自动化的采用增加
    • 扩大云端基础的服务的使用
    • 多个行业需要提高绩效和业务效率
  • 市场限制因素
    • 缺乏训练有素的专业人员
  • 市场机会
    • 透过认知运算、神经网路、深度学习技术和人工智慧 (AI) 的整合实现技术进步
    • 扩大医疗健康产业投资与合作
  • 市场挑战
    • 资料安全和隐私问题

波特五力：驾驭机器学习即服务市场的策略工具

波特的五力框架是理解市场竞争格局的重要工具。波特的五力框架为评估公司的竞争地位和探索策略机会提供了清晰的方法。该框架可帮助公司评估市场动态并确定新业务的盈利。这些见解使公司能够利用自己的优势，解决弱点并避免潜在的挑战，从而确保更强大的市场地位。

PESTLE分析：了解机器学习即服务市场的外部影响

外部宏观环境因素在塑造机器学习即服务市场的绩效动态方面发挥着至关重要的作用。对政治、经济、社会、技术、法律和环境因素的分析提供了应对这些影响所需的资讯。透过调查 PESTLE 因素，公司可以更了解潜在的风险和机会。这种分析可以帮助公司预测法规、消费者偏好和经济趋势的变化，并帮助他们做出积极主动的决策。

市场占有率分析 了解机器学习即服务市场的竞争格局

机器学习即服务市场的详细市场占有率分析可以对供应商绩效进行全面评估。公司可以透过比较收益、客户群和成长率等关键指标来揭示其竞争地位。该分析揭示了市场集中、分散和整合的趋势，为供应商提供了製定策略决策所需的洞察力，使他们能够在日益激烈的竞争中占有一席之地。

FPNV 机器学习即服务市场供应商的定位矩阵绩效评估

FPNV 定位矩阵是评估机器学习即服务市场供应商的重要工具。此矩阵允许业务组织根据供应商的商务策略和产品满意度评估供应商，从而做出符合其目标的明智决策。这四个象限使您能够清晰、准确地划分供应商，并确定最能满足您的策略目标的合作伙伴和解决方案。

策略分析与建议 规划您在机器学习即服务市场的成功之路

对于旨在加强其在全球市场的影响力的公司来说，对机器学习即服务市场的策略分析至关重要。透过审查关键资源、能力和绩效指标，公司可以识别成长机会并努力改进。这种方法使您能够克服竞争环境中的挑战，利用新的商机，并取得长期成功。

本报告对市场进行了全面分析，涵盖关键重点领域：

1. 市场渗透率：对当前市场环境的详细审查、主要企业的广泛资料、对其在市场中的影响力和整体影响力的评估。

2. 市场开拓：辨识新兴市场的成长机会，评估现有领域的扩张潜力，并提供未来成长的策略蓝图。

3. 市场多元化：分析近期产品发布、开拓地区、关键产业进展、塑造市场的策略投资。

4. 竞争评估与情报：彻底分析竞争格局，检验市场占有率、业务策略、产品系列、认证、监理核准、专利趋势、主要企业的技术进步等。

5. 产品开发与创新：重点在于有望推动未来市场成长的最尖端科技、研发活动和产品创新。

我们也回答重要问题，以帮助相关人员做出明智的决策：

1.目前的市场规模和未来的成长预测是多少？

2. 哪些产品、区隔市场和地区提供最佳投资机会？

3.塑造市场的主要技术趋势和监管影响是什么？

4.主要厂商的市场占有率和竞争地位如何？

5. 推动供应商市场进入和退出策略的收益来源和策略机会是什么？

目录

第一章 前言

第二章调查方法

第三章执行摘要

第四章市场概况

第五章市场洞察

  • 市场动态
    • 促进因素
      • 物联网和自动化的采用增加
      • 增加云端基础的服务的使用
      • 需要提高跨产业的绩效和营运效率
    • 抑制因素
      • 缺乏训练有素的专业人员
    • 机会
      • 整合认知运算、神经网路、深度学习技术和人工智慧 (AI) 的技术进步
      • 扩大医疗健康产业投资合作
    • 任务
      • 资料安全和隐私问题
  • 市场区隔分析
  • 波特五力分析
  • PESTEL分析
    • 政治的
    • 经济
    • 社群
    • 技术的
    • 合法地
    • 环境

第六章 机器学习即服务市场：依组成部分

  • 服务
  • 软体

第七章 机器学习即服务市场：依应用分类

  • 扩增实境和虚拟现实
  • 诈骗侦测和风险管理
  • 行销和广告
  • 预测分析
  • 安全和监视

第 8 章 机器学习即服务市场：依最终使用者划分

  • BFSI
  • 医疗保健和生命科学
  • 製造业
  • 零售
  • 通讯

第 9 章 美洲机器学习即服务市场

  • 阿根廷
  • 巴西
  • 加拿大
  • 墨西哥
  • 美国

第十章亚太地区机器学习即服务市场

  • 澳洲
  • 中国
  • 印度
  • 印尼
  • 日本
  • 马来西亚
  • 菲律宾
  • 新加坡
  • 韩国
  • 台湾
  • 泰国
  • 越南

第十一章欧洲、中东和非洲的机器学习即服务市场

  • 丹麦
  • 埃及
  • 芬兰
  • 法国
  • 德国
  • 以色列
  • 义大利
  • 荷兰
  • 奈及利亚
  • 挪威
  • 波兰
  • 卡达
  • 俄罗斯
  • 沙乌地阿拉伯
  • 南非
  • 西班牙
  • 瑞典
  • 瑞士
  • 土耳其
  • 阿拉伯聯合大公国
  • 英国

第十二章竞争格局

  • 2023 年市场占有率分析
  • FPNV 定位矩阵，2023
  • 竞争情境分析
  • 战略分析和建议

公司名单

  • 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