查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1593915

机器学习营运市场：按组件、部署、组织规模和最终用户 - 2025-2030 年全球预测

Machine Learning Operations Market by Component (Services, Software), Deployment (Cloud, On-Premise), Organization Size, End-User - Global Forecast 2025-2030
出版日期: | 出版商: 360iResearch | 英文 197 Pages | 商品交期: 最快1-2个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

2023年机器学习营运市场的市场规模为32.4亿美元，预计2024年将达到44.1亿美元，复合年增长率为36.22%，预计到2030年将达到282.6亿美元。

机器学习营运 (MLOps) 是资料科学中一个快速新兴的领域，它将 DevOps 原则与机器学习结合，以简化机器学习生命週期。这项需求源自于生产环境中部署、监控和维护机器学习模型的复杂性日益增加。随着人工智慧在医疗保健、金融和零售等行业的应用不断增加，MLOps 可确保 ML 模型的营运效率、可重复性和可扩展性。 MLOps 平台和工具透过自动化资料摄取、模型训练、检验和部署等流程来优化工作流程并减少瓶颈。该市场的主要推动因素是企业越来越多地采用人工智慧、提高模型准确性的需求以及由于巨量资料和云端运算的显着增长而导致的可扩展性需求的增加。随着各行业寻求利用先进的人工智慧技术增强决策和预测能力，预计它将获得关注。然而，整合复杂性、初始成本高和缺乏熟练人员等挑战可能会阻碍市场成长。此外，有关资料隐私的安全问题和合规问题仍然存在，为全面实施造成障碍。自动化机器学习、即时模型监控以及开发有助于与现有 IT 环境无缝整合的框架等领域都存在机会。鼓励公司投资开发混合云端平台，并加强资料科学家和 IT 营运人员之间的协作，以利用 MLOps。创新者应专注于改善开放原始码解决方案并开发强大的管治框架，以推动跨产业更广泛的采用。市场竞争激烈，但企业正在优先考虑敏捷性和效率，以改变先进分析在当今动态市场格局中提供见解和驱动资料主导决策的方式，其中之一是人工智慧营运的现代化。

主要市场统计
基准年[2023] 32.4亿美元
预测年份 [2024] 44.1亿美元
预测年份 [2030] 282.6亿美元
复合年增长率（%） 36.22%

市场动态：揭示快速发展的机器学习营运市场的关键市场洞察

机器学习营运市场正因供需的动态交互作用而转变。了解这些不断变化的市场动态可以帮助企业做出明智的投资决策、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，并了解消费行为及其对製造成本的影响，并更清楚地了解对采购趋势的影响。

  • 市场驱动因素
    • 扩大机器学习在製造业的应用
    • 政府努力实现最终用户部门的数位化和自动化，以提高生产力
    • 更加关注标准化机器学习流程以实现更好的管理
  • 市场限制因素
    • 由于差异而导致的与资料管理相关的问题
  • 市场机会
    • 不断改进机器学习操作并开发新的解决方案
    • 对智慧工厂和智慧製造技术的新投资
  • 市场挑战
    • 缺乏熟练且训练有素的专业人员

波特的五力：驾驭机器学习营运市场的策略工具

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：了解机器学习营运市场的外部影响

外部宏观环境因素在塑造机器学习营运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。这种分析可以帮助企业预测法规、消费者偏好和经济趋势的变化，并为他们做出积极主动的决策做好准备。

市场占有率分析 了解机器学习营运市场的竞争状况

机器学习营运市场的详细市场占有率分析提供了对供应商绩效的全面评估。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，以应对日益激烈的竞争。

FPNV定位矩阵机器学习营运市场厂商绩效评估

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

策略分析和建议绘製机器学习营运市场的成功之路

机器学习营运市场的策略分析对于旨在加强其在全球市场的影响力的公司至关重要。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，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
      • 扩大机器学习在製造业的应用
      • 政府推动最终用户部门数位化和自动化以提高生产力的倡议
      • 更加重视标准化机器学习流程以实现更好的管理
    • 抑制因素
      • 由于差异而导致的与资料管理相关的问题
    • 机会
      • 不断改进机器学习操作并开发新的解决方案
      • 对智慧工厂和智慧製造技术的新投资
    • 任务
      • 熟练且训练有素的专业人员数量有限
  • 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 6 章 机器学习营运市场：依组成部分

  • 服务
  • 软体

第 7 章 机器学习营运市场：按部署

  • 云
  • 本地

第 8 章 机器学习营运市场：依组织规模

  • 大公司
  • 小型企业

第 9 章 机器学习营运市场：依最终使用者划分

  • 航太和国防
  • 汽车/交通
  • 银行、金融服务和保险
  • 建筑、建筑、房地产
  • 消费品/零售
  • 教育
  • 能源/公共产业
  • 政府和公共部门
  • 医疗保健和生命科学
  • 资讯科技和通讯
  • 製造业
  • 媒体与娱乐
  • 旅游/酒店业

第十章美洲机器学习营运市场

  • 阿根廷
  • 巴西
  • 加拿大
  • 墨西哥
  • 美国

第十一章 亚太地区机器学习营运市场

  • 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

第十二章 欧洲、中东、非洲机器学习营运市场

  • 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

第十三章竞争格局

  • 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

  • Addepto Sp. z oo
  • Alibaba Cloud International
  • Allegro Artificial Intelligence Ltd.
  • Amazon Web Services, Inc.
  • Anyscale, Inc.
  • BigML Inc.
  • Canonical Ltd.
  • Dataiku
  • DataRobot, Inc.
  • Domino Data Lab, Inc.
  • Gathr Data Inc.
  • Google LLC by Alphabet Inc.
  • Grid Dynamics Holdings, Inc.
  • H2O.ai, Inc.
  • Hewlett Packard Enterprise Company
  • Iguazio Ltd. by McKinsey & Company
  • International Business Machines Corporation
  • Microsoft Corporation
  • Neal Analytics
  • Neptune Labs, Inc.
  • Neuro Inc.
  • Oracle Corporation
  • Runai Labs Ltd.
  • SAP SE
  • SAS Institute Inc.
  • Tredence Analytics Solutions Pvt. Ltd.
  • understandAI GmbH
  • Valohai
  • Virtusa Corporation
  • Weights and Biases, Inc.
Product Code: MRR-961BA04A2E4E

The Machine Learning Operations Market was valued at USD 3.24 billion in 2023, expected to reach USD 4.41 billion in 2024, and is projected to grow at a CAGR of 36.22%, to USD 28.26 billion by 2030.

Machine Learning Operations (MLOps) is a rapidly emerging discipline within data science that blends the principles of DevOps with machine learning to streamline the machine learning lifecycle. Its necessity stems from the growing complexities of deploying, monitoring, and maintaining machine learning models in production. With the rising implementation of AI across industries like healthcare, finance, and retail, MLOps ensures operational efficiency, reproducibility, and scalability of ML models. MLOps platforms and tools optimize workflows and reduce bottlenecks by automating processes such as data ingestion, model training, validation, and deployment, leading to faster model updates and better performance. The market is primarily fueled by increasing AI adoption in businesses, the necessity for improving model accuracy, and greater demand for scalability aligning with substantial growth in big data and cloud computing. It's projected to gain notably as industries seek to enhance decision-making and predictive capabilities through advanced AI technologies. However, challenges such as integration complexity, high initial costs, and the lack of skilled personnel can impede market growth. Security concerns and compliance issues related to data privacy also linger, presenting barriers to full-scale adoption. Opportunities lie in sectors like automated ML, real-time model monitoring, and the development of frameworks that facilitate seamless integration with existing IT environments. Firms are advised to invest in developing hybrid cloud platforms and enhancing collaboration between data scientists and IT operations to capitalize on MLOps benefits. Innovators should focus on improving open-source solutions and developing robust governance frameworks to drive broader adoption across different industries. The market is competitive yet promises modernization of AI operations, as businesses prioritize agility and efficiency, transforming how advanced analytics deliver insights and foster data-driven decision-making in today's dynamic market landscape.

KEY MARKET STATISTICS
Base Year [2023] USD 3.24 billion
Estimated Year [2024] USD 4.41 billion
Forecast Year [2030] USD 28.26 billion
CAGR (%) 36.22%

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Machine Learning Operations Market

The Machine Learning Operations 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
    • Increasing utilization of machine learning in the manufacturing sector
    • Government initiatives to digitalize and automate end-user sectors to boost productivity
    • Growing focus on standardization of machine learning processes for better management
  • Market Restraints
    • Issues associated with data management due to discrepancies
  • Market Opportunities
    • Continuous improvements in machine learning operations and development of new solutions
    • New investments in smart factory and smart manufacturing technologies
  • Market Challenges
    • Limited availability of skilled and trained professionals

Porter's Five Forces: A Strategic Tool for Navigating the Machine Learning Operations Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Machine Learning Operations 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 Operations Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Machine Learning Operations 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 Operations Market

A detailed market share analysis in the Machine Learning Operations 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 Operations Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Machine Learning Operations 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 Operations Market

A strategic analysis of the Machine Learning Operations 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 Operations Market, highlighting leading vendors and their innovative profiles. These include Addepto Sp. z o. o., Alibaba Cloud International, Allegro Artificial Intelligence Ltd., Amazon Web Services, Inc., Anyscale, Inc., BigML Inc., Canonical Ltd., Dataiku, DataRobot, Inc., Domino Data Lab, Inc., Gathr Data Inc., Google LLC by Alphabet Inc., Grid Dynamics Holdings, Inc., H2O.ai, Inc., Hewlett Packard Enterprise Company, Iguazio Ltd. by McKinsey & Company, International Business Machines Corporation, Microsoft Corporation, Neal Analytics, Neptune Labs, Inc., Neuro Inc., Oracle Corporation, Runai Labs Ltd., SAP SE, SAS Institute Inc., Tredence Analytics Solutions Pvt. Ltd., understandAI GmbH, Valohai, Virtusa Corporation, and Weights and Biases, Inc..

Market Segmentation & Coverage

This research report categorizes the Machine Learning Operations 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 Deployment, market is studied across Cloud and On-Premise.
  • Based on Organization Size, market is studied across Large Enterprises and SMEs.
  • Based on End-User, market is studied across Aerospace & Defense, Automotive & Transportation, Banking, Financial Services & Insurance, Building, Construction & Real Estate, Consumer Goods & Retail, Education, Energy & Utilities, Government & Public Sector, Healthcare & Life Sciences, Information Technology & Telecommunication, Manufacturing, Media & Entertainment, and Travel & Hospitality.
  • 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. Increasing utilization of machine learning in the manufacturing sector
      • 5.1.1.2. Government initiatives to digitalize and automate end-user sectors to boost productivity
      • 5.1.1.3. Growing focus on standardization of machine learning processes for better management
    • 5.1.2. Restraints
      • 5.1.2.1. Issues associated with data management due to discrepancies
    • 5.1.3. Opportunities
      • 5.1.3.1. Continuous improvements in machine learning operations and development of new solutions
      • 5.1.3.2. New investments in smart factory and smart manufacturing technologies
    • 5.1.4. Challenges
      • 5.1.4.1. Limited availability of skilled and trained professionals
  • 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 Operations Market, by Component

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

7. Machine Learning Operations Market, by Deployment

  • 7.1. Introduction
  • 7.2. Cloud
  • 7.3. On-Premise

8. Machine Learning Operations Market, by Organization Size

  • 8.1. Introduction
  • 8.2. Large Enterprises
  • 8.3. SMEs

9. Machine Learning Operations Market, by End-User

  • 9.1. Introduction
  • 9.2. Aerospace & Defense
  • 9.3. Automotive & Transportation
  • 9.4. Banking, Financial Services & Insurance
  • 9.5. Building, Construction & Real Estate
  • 9.6. Consumer Goods & Retail
  • 9.7. Education
  • 9.8. Energy & Utilities
  • 9.9. Government & Public Sector
  • 9.10. Healthcare & Life Sciences
  • 9.11. Information Technology & Telecommunication
  • 9.12. Manufacturing
  • 9.13. Media & Entertainment
  • 9.14. Travel & Hospitality

10. Americas Machine Learning Operations Market

  • 10.1. Introduction
  • 10.2. Argentina
  • 10.3. Brazil
  • 10.4. Canada
  • 10.5. Mexico
  • 10.6. United States

11. Asia-Pacific Machine Learning Operations Market

  • 11.1. Introduction
  • 11.2. Australia
  • 11.3. China
  • 11.4. India
  • 11.5. Indonesia
  • 11.6. Japan
  • 11.7. Malaysia
  • 11.8. Philippines
  • 11.9. Singapore
  • 11.10. South Korea
  • 11.11. Taiwan
  • 11.12. Thailand
  • 11.13. Vietnam

12. Europe, Middle East & Africa Machine Learning Operations Market

  • 12.1. Introduction
  • 12.2. Denmark
  • 12.3. Egypt
  • 12.4. Finland
  • 12.5. France
  • 12.6. Germany
  • 12.7. Israel
  • 12.8. Italy
  • 12.9. Netherlands
  • 12.10. Nigeria
  • 12.11. Norway
  • 12.12. Poland
  • 12.13. Qatar
  • 12.14. Russia
  • 12.15. Saudi Arabia
  • 12.16. South Africa
  • 12.17. Spain
  • 12.18. Sweden
  • 12.19. Switzerland
  • 12.20. Turkey
  • 12.21. United Arab Emirates
  • 12.22. United Kingdom

13. Competitive Landscape

  • 13.1. Market Share Analysis, 2023
  • 13.2. FPNV Positioning Matrix, 2023
  • 13.3. Competitive Scenario Analysis
  • 13.4. Strategy Analysis & Recommendation

Companies Mentioned

  • 1. Addepto Sp. z o. o.
  • 2. Alibaba Cloud International
  • 3. Allegro Artificial Intelligence Ltd.
  • 4. Amazon Web Services, Inc.
  • 5. Anyscale, Inc.
  • 6. BigML Inc.
  • 7. Canonical Ltd.
  • 8. Dataiku
  • 9. DataRobot, Inc.
  • 10. Domino Data Lab, Inc.
  • 11. Gathr Data Inc.
  • 12. Google LLC by Alphabet Inc.
  • 13. Grid Dynamics Holdings, Inc.
  • 14. H2O.ai, Inc.
  • 15. Hewlett Packard Enterprise Company
  • 16. Iguazio Ltd. by McKinsey & Company
  • 17. International Business Machines Corporation
  • 18. Microsoft Corporation
  • 19. Neal Analytics
  • 20. Neptune Labs, Inc.
  • 21. Neuro Inc.
  • 22. Oracle Corporation
  • 23. Runai Labs Ltd.
  • 24. SAP SE
  • 25. SAS Institute Inc.
  • 26. Tredence Analytics Solutions Pvt. Ltd.
  • 27. understandAI GmbH
  • 28. Valohai
  • 29. Virtusa Corporation
  • 30. Weights and Biases, Inc.

LIST OF FIGURES

  • FIGURE 1. MACHINE LEARNING OPERATIONS MARKET RESEARCH PROCESS
  • FIGURE 2. MACHINE LEARNING OPERATIONS MARKET SIZE, 2023 VS 2030
  • FIGURE 3. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, 2018-2030 (USD MILLION)
  • FIGURE 4. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY REGION, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 5. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 6. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2023 VS 2030 (%)
  • FIGURE 7. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 8. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2023 VS 2030 (%)
  • FIGURE 9. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 10. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2023 VS 2030 (%)
  • FIGURE 11. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 12. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2023 VS 2030 (%)
  • FIGURE 13. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 14. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 15. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 16. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY STATE, 2023 VS 2030 (%)
  • FIGURE 17. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY STATE, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 18. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 19. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 20. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2030 (%)
  • FIGURE 21. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2023 VS 2024 VS 2030 (USD MILLION)
  • FIGURE 22. MACHINE LEARNING OPERATIONS MARKET SHARE, BY KEY PLAYER, 2023
  • FIGURE 23. MACHINE LEARNING OPERATIONS MARKET, FPNV POSITIONING MATRIX, 2023

LIST OF TABLES

  • TABLE 1. MACHINE LEARNING OPERATIONS MARKET SEGMENTATION & COVERAGE
  • TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2023
  • TABLE 3. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, 2018-2030 (USD MILLION)
  • TABLE 4. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 5. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 6. MACHINE LEARNING OPERATIONS MARKET DYNAMICS
  • TABLE 7. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 8. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY SERVICES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 9. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY SOFTWARE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 10. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 11. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY CLOUD, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 12. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ON-PREMISE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 13. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 14. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY LARGE ENTERPRISES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 15. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY SMES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 16. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 17. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY AEROSPACE & DEFENSE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 18. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY AUTOMOTIVE & TRANSPORTATION, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 19. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY BANKING, FINANCIAL SERVICES & INSURANCE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 20. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY BUILDING, CONSTRUCTION & REAL ESTATE, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 21. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY CONSUMER GOODS & RETAIL, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 22. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY EDUCATION, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 23. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ENERGY & UTILITIES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 24. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY GOVERNMENT & PUBLIC SECTOR, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 25. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY HEALTHCARE & LIFE SCIENCES, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 26. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY INFORMATION TECHNOLOGY & TELECOMMUNICATION, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 27. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY MANUFACTURING, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 28. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY MEDIA & ENTERTAINMENT, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 29. GLOBAL MACHINE LEARNING OPERATIONS MARKET SIZE, BY TRAVEL & HOSPITALITY, BY REGION, 2018-2030 (USD MILLION)
  • TABLE 30. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 31. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 32. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 33. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 34. AMERICAS MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 35. ARGENTINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 36. ARGENTINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 37. ARGENTINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 38. ARGENTINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 39. BRAZIL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 40. BRAZIL MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 41. BRAZIL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 42. BRAZIL MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 43. CANADA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 44. CANADA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 45. CANADA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 46. CANADA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 47. MEXICO MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 48. MEXICO MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 49. MEXICO MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 50. MEXICO MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 51. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 52. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 53. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 54. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 55. UNITED STATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY STATE, 2018-2030 (USD MILLION)
  • TABLE 56. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 57. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 58. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 59. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 60. ASIA-PACIFIC MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 61. AUSTRALIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 62. AUSTRALIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 63. AUSTRALIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 64. AUSTRALIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 65. CHINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 66. CHINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 67. CHINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 68. CHINA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 69. INDIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 70. INDIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 71. INDIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 72. INDIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 73. INDONESIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 74. INDONESIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 75. INDONESIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 76. INDONESIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 77. JAPAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 78. JAPAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 79. JAPAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 80. JAPAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 81. MALAYSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 82. MALAYSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 83. MALAYSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 84. MALAYSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 85. PHILIPPINES MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 86. PHILIPPINES MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 87. PHILIPPINES MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 88. PHILIPPINES MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 89. SINGAPORE MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 90. SINGAPORE MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 91. SINGAPORE MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 92. SINGAPORE MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 93. SOUTH KOREA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 94. SOUTH KOREA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 95. SOUTH KOREA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 96. SOUTH KOREA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 97. TAIWAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 98. TAIWAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 99. TAIWAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 100. TAIWAN MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 101. THAILAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 102. THAILAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 103. THAILAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 104. THAILAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 105. VIETNAM MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 106. VIETNAM MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 107. VIETNAM MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 108. VIETNAM MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 109. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 110. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 111. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 112. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 113. EUROPE, MIDDLE EAST & AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COUNTRY, 2018-2030 (USD MILLION)
  • TABLE 114. DENMARK MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 115. DENMARK MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 116. DENMARK MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 117. DENMARK MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 118. EGYPT MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 119. EGYPT MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 120. EGYPT MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 121. EGYPT MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 122. FINLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 123. FINLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 124. FINLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 125. FINLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 126. FRANCE MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 127. FRANCE MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 128. FRANCE MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 129. FRANCE MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 130. GERMANY MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 131. GERMANY MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 132. GERMANY MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 133. GERMANY MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 134. ISRAEL MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 135. ISRAEL MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 136. ISRAEL MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 137. ISRAEL MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 138. ITALY MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 139. ITALY MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 140. ITALY MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 141. ITALY MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 142. NETHERLANDS MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 143. NETHERLANDS MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 144. NETHERLANDS MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 145. NETHERLANDS MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 146. NIGERIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 147. NIGERIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 148. NIGERIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 149. NIGERIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 150. NORWAY MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 151. NORWAY MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 152. NORWAY MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 153. NORWAY MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 154. POLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 155. POLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 156. POLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 157. POLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 158. QATAR MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 159. QATAR MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 160. QATAR MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 161. QATAR MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 162. RUSSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 163. RUSSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 164. RUSSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 165. RUSSIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 166. SAUDI ARABIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 167. SAUDI ARABIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 168. SAUDI ARABIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 169. SAUDI ARABIA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 170. SOUTH AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 171. SOUTH AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 172. SOUTH AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 173. SOUTH AFRICA MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 174. SPAIN MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 175. SPAIN MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 176. SPAIN MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 177. SPAIN MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 178. SWEDEN MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 179. SWEDEN MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 180. SWEDEN MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 181. SWEDEN MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 182. SWITZERLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 183. SWITZERLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 184. SWITZERLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 185. SWITZERLAND MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 186. TURKEY MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 187. TURKEY MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 188. TURKEY MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 189. TURKEY MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 190. UNITED ARAB EMIRATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 191. UNITED ARAB EMIRATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 192. UNITED ARAB EMIRATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 193. UNITED ARAB EMIRATES MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 194. UNITED KINGDOM MACHINE LEARNING OPERATIONS MARKET SIZE, BY COMPONENT, 2018-2030 (USD MILLION)
  • TABLE 195. UNITED KINGDOM MACHINE LEARNING OPERATIONS MARKET SIZE, BY DEPLOYMENT, 2018-2030 (USD MILLION)
  • TABLE 196. UNITED KINGDOM MACHINE LEARNING OPERATIONS MARKET SIZE, BY ORGANIZATION SIZE, 2018-2030 (USD MILLION)
  • TABLE 197. UNITED KINGDOM MACHINE LEARNING OPERATIONS MARKET SIZE, BY END-USER, 2018-2030 (USD MILLION)
  • TABLE 198. MACHINE LEARNING OPERATIONS MARKET SHARE, BY KEY PLAYER, 2023
  • TABLE 199. MACHINE LEARNING OPERATIONS MARKET, FPNV POSITIONING MATRIX, 2023