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市场调查报告书
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1716289

2032 年机器学习 (ML) 市场预测：按组件、公司规模、部署、应用、最终用户和地区进行的全球分析

Machine Learning (ML) Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software and Services), Enterprise Size (SMEs and Large Enterprises), Deployment, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球机器学习 (ML) 市场预计在 2025 年达到 860.2 亿美元，到 2032 年将达到 6,266.2 亿美元，预测期内的复合年增长率为 32.8%。

人工智慧的一个分支，称为机器学习 (ML)，它使电脑能够从资料中学习并做出决策和预测，而无需明确编程。使用统计模型和演算法来发现大型资料集中的模式并随着时间的推移提高效能。机器学习 (ML) 广泛应用于行销、金融、医疗保健和自主系统等许多行业，以提高生产力和决策能力。此外，影像识别、自然语言处理和推荐系统等复杂问题也可以由机器学习模型处理。

一项针对全球数据专业人士的调查发现，45% 的企业已经采用了机器学习技术，另有 21% 的企业正在探索其用途。采用率因国家而异，以色列为 63%，荷兰为 57%，美国为 56%。大型公司（61%）的采用率高于中型公司（45%）和中小型公司（33%）。

数据製造的成长

各行各业日益增长的数位转型推动了社交媒体、企业应用程式、电子商务平台和物联网设备等各种来源的数据爆炸性增长。如此大量的结构化和非结构化资料对于组织来说很难手动处理和分析。借助机器学习演算法，企业可以发现模式，获得有价值的见解，并做出即时数据主导的决策。此外，巨量资料分析平台的出现也进一步加速了机器学习解决方案的采用。

复杂性和实施成本高

儘管机器学习具有长期效益，但对于许多企业来说，基础设施、熟练员工和模型培训所需的初始投资可能过高。为了成功开发和实施机器学习模型，公司需要投资强大的运算资源、良好的资料集和尖端的软体工具。此外，将机器学习 (ML) 与目前的企业系统结合非常困难，需要专门的解决方案，从而进一步增加成本。中小企业 (SME) 无法为机器学习 (ML)计划提供资金，这减缓了其采用速度。

对基于人工智慧的网路安全解决方案的需求日益增加

网路攻击的日益复杂和频繁推动了对基于机器学习的网路安全解决方案的需求日益增长。支援人工智慧的安全系统可以识别诈欺、预测威胁并自动进行即时回应以降低风险。为了加强网路防御，机器学习演算法正在应用于网路安全监控、诈骗侦测和身份验证。此外，随着政府和企业优先考虑网路安全投资，基于机器学习 (ML) 的威胁情报平台和行为分析解决方案代表着市场扩张的机会。

依赖获取高品质数据

ML 效能在很大程度上依赖公正、多样化和高品质的数据。然而，许多行业缺乏合适或高品质的数据集，限制了人工智慧模型的有效性。资料碎片化、来源不一致以及隐私限制是导致资料收集困难的一些因素。此外，为了保持准确性，机器学习模型必须经常使用新资料进行更新，但资料存取通常受到专有或监管限制。如果没有足够可靠的资料集，机器学习模型可能会产生过时、错误或误导性的见解，从而降低其对业务的整体效用。

COVID-19的影响：

随着企业和组织寻求创造性的方法来应对疫情的影响，COVID-19 疫情显着加速了许多产业对机器学习 (ML) 的采用。数据分析、预测模型和即时决策需要更先进的人工智慧驱动系统，对远端工作、数位服务和自动化的依赖性不断增加就证明了这一点。在医疗领域，机器学习在开发疫苗、诊断设备和改善患者照护发挥了关键作用。金融服务、供应链管理和电子商务也在使用机器学习 (ML) 来检测诈欺、预测需求并提供个人化的客户体验。此外，疫情也凸显了演算法偏见、资料隐私问题以及熟练的机器学习专家短缺等问题。

预测分析领域预计将成为预测期内最大的领域

预计预测期内预测分析部分将占据最大的市场占有率。这一领域对零售、医疗保健和金融等许多行业都至关重要，因为它使用机器学习演算法来预测未来结果并分析历史数据。预测分析使企业能够预测趋势、客户行为和业务需求，支援更好的决策、流程优化和客户体验。需求预测、风险管理和库存优化只是其众多用途的一部分。此外，预测分析主导机器学习市场的主要原因之一是对数据主导洞察力的日益依赖以及跨行业产生的数据量。

预计医疗保健领域在预测期内将以最高的复合年增长率成长。

机器学习正在透过实现个人化医疗、提高诊断准确性和改善患者治疗效果来彻底改变医疗保健。 ML 分析大量医疗数据的能力有助于早期疾病检测、治疗优化和药物发现。此外，机器学习演算法也被用于医疗保健自动化、预测分析和医学成像，以提高效率并显着降低成本。随着医疗保健提供者继续拥抱数位转型，未来几年对先进机器学习解决方案的需求可能会大幅增加，以解决棘手问题并加强患者照护。

占比最大的地区：

预计北美地区将在预测期内占据最大的市场占有率。重要科技公司的存在、先进的基础设施以及大量的研发支出是这项优势的主要因素。大量新兴企业和老字型大小企业正在医疗保健、金融、IT 和汽车等行业实施 ML 解决方案，使该地区成为 AI 和 ML 创新的中心。机器学习的采用率不断提高也为政府专案和人工智慧技术带来了更多资金筹措。此外，北美凭藉其强大的数据收集和处理能力以及高度的技术意识，引领全球机器学习市场。

复合年增长率最高的地区：

预计亚太地区在预测期内的复合年增长率最高。这一成长是由快速的数位转型、人工智慧技术的日益普及以及对数据分析的大规模投资所推动的。中国、印度和日本等国家正努力将机器学习 (ML) 融入製造业、金融和医疗保健等各个产业。此外，该地区庞大的人口、不断增长的智慧型手机普及率以及不断扩大的网路连接进一步刺激了对机器学习解决方案的需求。亚太地区是机器学习应用成长最快的地区，这得益于政府推广智慧技术的措施以及国际科技公司的崛起。

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  • 地理细分
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目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 次级研究资讯来源
    • 先决条件

第三章市场走势分析

  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第五章 全球机器学习（ML）市场（按组件）

  • 硬体
  • 软体
  • 服务

第六章 全球机器学习（ML）市场（依公司规模）

  • 中小企业
  • 大型企业

第七章 全球机器学习（ML）市场（按部署）

  • 云
  • 本地

8. 全球机器学习（ML）市场（按应用）

  • 预测分析
  • 自然语言处理（NLP）
  • 影像识别
  • 诈欺侦测
  • 其他应用

第九章 全球机器学习（ML）市场（按最终用户）

  • 卫生保健
  • 银行、金融服务和保险（BFSI）
  • 资讯科技/通讯
  • 广告和媒体
  • 汽车与运输
  • 农业
  • 能源与公共产业
  • 製造业
  • 其他最终用户

第十章 全球机器学习（ML）市场（按地区）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • Amazon Web Services, Inc.
  • IBM Corporation
  • Microsoft Corporation
  • SAP SE
  • Google Cloud
  • Xicom Technologies
  • Nvidia Inc
  • Vention
  • Intel Corporation
  • SAS Institute Inc.
  • Hewlett Packard Enterprise Company
  • Oracle Corporation
  • Altoros
  • MobiDev
  • BigML, Inc.
Product Code: SMRC29101

According to Stratistics MRC, the Global Machine Learning (ML) Market is accounted for $86.02 billion in 2025 and is expected to reach $626.62 billion by 2032 growing at a CAGR of 32.8% during the forecast period. Computers can learn from data and make decisions or predictions without explicit programming owing to a subfield of artificial intelligence called machine learning (ML). Through the use of statistical models and algorithms, it finds patterns in large datasets, gradually improving performance. Machine learning (ML) is widely used in many industries, such as marketing, finance, healthcare, and autonomous systems, where it improves productivity and decision-making. Moreover, complex issues like image recognition, natural language processing, and recommendation systems can be handled by ML models.

According to a worldwide survey of data professionals, 45% of companies have adopted machine learning methods, with an additional 21% exploring their use. Adoption rates vary by country, with Israel at 63%, the Netherlands at 57%, and the United States at 56%. Larger enterprises show higher adoption rates (61%) compared to medium (45%) and small companies (33%).

Market Dynamics:

Driver:

Growth in data manufacturing

An explosion of data generation from various sources, such as social media, enterprise apps, e-commerce platforms, and Internet of Things devices, has resulted from the rise of digital transformation across industries. This massive volume of structured and unstructured data is difficult for organizations to manually process and analyze. Businesses can find patterns, derive valuable insights, and make data-driven decisions instantly with the aid of machine learning algorithms. Additionally, the adoption of machine learning solutions has been further accelerated by the availability of big data analytics platforms.

Restraint:

High complexity and implementation costs

The initial investment needed for infrastructure, qualified staff, and model training can be too high for many businesses, even though machine learning has long-term advantages. For businesses to successfully develop and implement ML models, they must invest in strong computer resources, superior datasets, and cutting-edge software tools. Furthermore, integrating machine learning (ML) with current enterprise systems can be challenging and necessitate specialized solutions, which raise costs even more. Widespread adoption is slowed by small and medium-sized businesses' (SMEs') inability to devote funds to machine learning (ML) projects.

Opportunity:

Growing need for cyber security solutions powered by AI

There is a significant need for ML-driven cyber security solutions due to the growing sophistication and frequency of cyber attacks. Security systems with AI capabilities are able to identify irregularities, anticipate threats, and automate in-the-moment reactions to reduce risks. To bolster cyber defenses, machine learning algorithms are being applied to network security monitoring, fraud detection, and identity verification. Moreover, machine learning (ML)-powered threat intelligence platforms and behavioral analytics solutions offer a profitable opportunity for market expansion as governments and corporations prioritize cyber security investments.

Threat:

Reliance on access to high-quality data

ML performance is highly dependent on unbiased, diverse, and high-quality data. The efficacy of AI models is, however, constrained by the lack of adequate or high-quality datasets in many industries. Data fragmentation, inconsistencies between sources, and privacy restrictions are some of the factors that make data collection difficult. Furthermore, in order to maintain accuracy, machine learning models need to be updated frequently with new data; however, data access is frequently restricted by proprietary and regulatory constraints. Without sufficient and trustworthy datasets, machine learning models run the risk of generating outdated, false, or misleading insights, which lowers their overall usefulness to companies.

Covid-19 Impact:

The COVID-19 pandemic greatly sped up the adoption of machine learning (ML) in a number of industries as companies and organizations looked for creative ways to deal with disruptions. For data analysis, predictive modelling, and real-time decision-making, more sophisticated AI-driven systems are required, as evidenced by the growing dependence on remote work, digital services, and automation. In the medical field, machine learning played a key role in the creation of vaccines, diagnostic instruments, and patient care enhancement. Financial services, supply chain management, and e-commerce have also used machine learning (ML) to detect fraud, forecast demand, and provide individualized customer experiences. Moreover, problems like algorithmic bias, data privacy issues, and a lack of qualified machine learning specialists were also brought to light by the pandemic.

The predictive analytics segment is expected to be the largest during the forecast period

The predictive analytics segment is expected to account for the largest market share during the forecast period. This segment is crucial to a number of industries, including retail, healthcare, and finance, because it uses machine learning algorithms to forecast future results and analyze historical data. Predictive analytics aids in better decision-making, process optimization, and customer experience by empowering companies to forecast trends, customer behavior, and operational requirements. Demand forecasting, risk management, and inventory optimization are just a few of its many uses. Additionally, one of the main reasons predictive analytics is dominating the machine learning market is the growing reliance on data-driven insights and the volume of data generated across industries.

The healthcare segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare segment is predicted to witness the highest growth rate because it allows for personalized medicine, increases diagnostic precision, and improves patient outcomes, machine learning is revolutionizing the healthcare sector. ML is helping with early disease detection, treatment optimization, and drug discovery because of its capacity to analyze enormous volumes of medical data. Furthermore, ML algorithms are being used to improve efficiency and drastically lower costs in healthcare automation, predictive analytics, and medical imaging. In the upcoming years, there will likely be a significant increase in demand for sophisticated machine learning solutions to handle challenging problems and enhance patient care as healthcare providers continue to embrace digital transformation.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share. The existence of important technology companies, sophisticated infrastructure, and large R&D expenditures are the main drivers of this dominance. Numerous startups and well-established businesses are implementing ML solutions in a variety of industries, including healthcare, finance, IT, and automotive, making the region a center for AI and ML innovation. The rise in ML adoption has also been aided by government programs and more financing for AI-powered technologies. Moreover, North America leads the global machine learning market due to its strong data collection and processing capabilities and high degree of technological awareness.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR. This growth is being driven by the quick digital transformation, growing use of AI technologies, and large investments in data analytics. Countries like China, India, and Japan are putting a lot of effort into integrating machine learning (ML) into a variety of industries, such as manufacturing, finance, and healthcare. Additionally, the demand for ML solutions is further fueled by the region's sizable population, increasing smartphone adoption, and expanding internet connectivity. Asia-Pacific is now the fastest-growing region for machine learning adoption due to government initiatives to promote smart technologies and the growing number of international tech companies in the region.

Key players in the market

Some of the key players in Machine Learning (ML) Market include Amazon Web Services, Inc., IBM Corporation, Microsoft Corporation, SAP SE, Google Cloud, Xicom Technologies, Nvidia Inc, Vention, Intel Corporation, SAS Institute Inc., Hewlett Packard Enterprise Company, Oracle Corporation, Altoros, MobiDev and BigML, Inc.

Key Developments:

In December 2024, Amazon Web Services (AWS) and Atlassian Corporation announced a multi-year strategic collaboration agreement (SCA) to expedite cloud transformation and deliver advanced AI and security capabilities to enterprise customers. The SCA will help drive the migration of millions of enterprise users from Atlassian's Data Center business - which generates over $1 billion in annual revenue - to Atlassian Cloud over a multi-year timeline.

In July 2024, IBM announced that it has secured a five-year contract with $26 million in initial funding from the U.S. Agency for International Development (USAID) to support its Cybersecurity Protection and Response (CPR) program aimed to expand and enhance the agency's cybersecurity response support for host governments in the Europe and Eurasia (E&E) region.

In June 2024, Microsoft Corp. and Hitachi Ltd. announced a projected multibillion-dollar collaboration over the next three years that will accelerate social innovation with generative AI. Through this strategic alliance, Hitachi will propel growth of the Lumada business, with a planned revenue of 2.65 trillion yen (18.9 billion USD)*1 in FY2024, and will promote operational efficiency and productivity improvements for Hitachi Group's 270,000 employees.

Components Covered:

  • Hardware
  • Software
  • Services

Enterprise Sizes Covered:

  • SMEs
  • Large Enterprises

Deployments Covered:

  • Cloud
  • On-premise

Applications Covered:

  • Predictive Analytics
  • Natural Language Processing (NLP)
  • Image Recognition
  • Fraud Detection
  • Other Applications

End Users Covered:

  • Healthcare
  • Banking, Financial Services and Insurance (BFSI)
  • IT and Telecommunication
  • Advertising & Media
  • Automotive & Transportation
  • Agriculture
  • Energy & Utilities
  • Manufacturing
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Machine Learning (ML) Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Services

6 Global Machine Learning (ML) Market, By Enterprise Size

  • 6.1 Introduction
  • 6.2 SMEs
  • 6.3 Large Enterprises

7 Global Machine Learning (ML) Market, By Deployment

  • 7.1 Introduction
  • 7.2 Cloud
  • 7.3 On-premise

8 Global Machine Learning (ML) Market, By Application

  • 8.1 Introduction
  • 8.2 Predictive Analytics
  • 8.3 Natural Language Processing (NLP)
  • 8.4 Image Recognition
  • 8.5 Fraud Detection
  • 8.6 Other Applications

9 Global Machine Learning (ML) Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare
  • 9.3 Banking, Financial Services and Insurance (BFSI)
  • 9.4 IT and Telecommunication
  • 9.5 Advertising & Media
  • 9.6 Automotive & Transportation
  • 9.7 Agriculture
  • 9.8 Energy & Utilities
  • 9.9 Manufacturing
  • 9.10 Other End Users

10 Global Machine Learning (ML) Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Amazon Web Services, Inc.
  • 12.2 IBM Corporation
  • 12.3 Microsoft Corporation
  • 12.4 SAP SE
  • 12.5 Google Cloud
  • 12.6 Xicom Technologies
  • 12.7 Nvidia Inc
  • 12.8 Vention
  • 12.9 Intel Corporation
  • 12.10 SAS Institute Inc.
  • 12.11 Hewlett Packard Enterprise Company
  • 12.12 Oracle Corporation
  • 12.13 Altoros
  • 12.14 MobiDev
  • 12.15 BigML, Inc.

List of Tables

  • Table 1 Global Machine Learning (ML) Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Machine Learning (ML) Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global Machine Learning (ML) Market Outlook, By Hardware (2024-2032) ($MN)
  • Table 4 Global Machine Learning (ML) Market Outlook, By Software (2024-2032) ($MN)
  • Table 5 Global Machine Learning (ML) Market Outlook, By Services (2024-2032) ($MN)
  • Table 6 Global Machine Learning (ML) Market Outlook, By Enterprise Size (2024-2032) ($MN)
  • Table 7 Global Machine Learning (ML) Market Outlook, By SMEs (2024-2032) ($MN)
  • Table 8 Global Machine Learning (ML) Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 9 Global Machine Learning (ML) Market Outlook, By Deployment (2024-2032) ($MN)
  • Table 10 Global Machine Learning (ML) Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 11 Global Machine Learning (ML) Market Outlook, By On-premise (2024-2032) ($MN)
  • Table 12 Global Machine Learning (ML) Market Outlook, By Application (2024-2032) ($MN)
  • Table 13 Global Machine Learning (ML) Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 14 Global Machine Learning (ML) Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 15 Global Machine Learning (ML) Market Outlook, By Image Recognition (2024-2032) ($MN)
  • Table 16 Global Machine Learning (ML) Market Outlook, By Fraud Detection (2024-2032) ($MN)
  • Table 17 Global Machine Learning (ML) Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 18 Global Machine Learning (ML) Market Outlook, By End User (2024-2032) ($MN)
  • Table 19 Global Machine Learning (ML) Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 20 Global Machine Learning (ML) Market Outlook, By Banking, Financial Services and Insurance (BFSI) (2024-2032) ($MN)
  • Table 21 Global Machine Learning (ML) Market Outlook, By IT and Telecommunication (2024-2032) ($MN)
  • Table 22 Global Machine Learning (ML) Market Outlook, By Advertising & Media (2024-2032) ($MN)
  • Table 23 Global Machine Learning (ML) Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
  • Table 24 Global Machine Learning (ML) Market Outlook, By Agriculture (2024-2032) ($MN)
  • Table 25 Global Machine Learning (ML) Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 26 Global Machine Learning (ML) Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 27 Global Machine Learning (ML) Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.