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1716289

2032 年機器學習 (ML) 市場預測：按組件、公司規模、部署、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球機器學習 (ML) 市場預計在 2025 年達到 860.2 億美元，到 2032 年將達到 6,266.2 億美元，預測期內的複合年成長率為 32.8%。

人工智慧的一個分支，稱為機器學習 (ML)，它使電腦能夠從資料中學習並做出決策和預測，而無需明確編程。使用統計模型和演算法來發現大型資料集中的模式並隨著時間的推移提高效能。機器學習 (ML) 廣泛應用於行銷、金融、醫療保健和自主系統等許多行業，以提高生產力和決策能力。此外，影像識別、自然語言處理和推薦系統等複雜問題也可以由機器學習模型處理。

一項針對全球數據專業人士的調查發現，45% 的企業已經採用了機器學習技術，另有 21% 的企業正在探索其用途。採用率因國家而異，以色列為 63%，荷蘭為 57%，美國為 56%。大型公司（61%）的採用率高於中型公司（45%）和中小型公司（33%）。

數據製造的成長

各行各業日益成長的數位轉型推動了社交媒體、企業應用程式、電子商務平台和物聯網設備等各種來源的數據爆炸性成長。如此大量的結構化和非結構化資料對於組織來說很難手動處理和分析。借助機器學習演算法，企業可以發現模式，獲得有價值的見解，並做出即時數據主導的決策。此外，巨量資料分析平台的出現也進一步加速了機器學習解決方案的採用。

複雜性和實施成本高

儘管機器學習具有長期效益，但對於許多企業來說，基礎設施、熟練員工和模型培訓所需的初始投資可能過高。為了成功開發和實施機器學習模型，公司需要投資強大的運算資源、良好的資料集和尖端的軟體工具。此外，將機器學習 (ML) 與目前的企業系統結合非常困難，需要專門的解決方案，從而進一步增加成本。中小企業 (SME) 無法為機器學習 (ML)計劃提供資金，這減緩了其採用速度。

對基於人工智慧的網路安全解決方案的需求日益增加

網路攻擊的日益複雜和頻繁推動了對基於機器學習的網路安全解決方案的需求日益成長。支援人工智慧的安全系統可以識別詐欺、預測威脅並自動進行即時回應以降低風險。為了加強網路防禦，機器學習演算法正在應用於網路安全監控、詐騙偵測和身份驗證。此外，隨著政府和企業優先考慮網路安全投資，基於機器學習 (ML) 的威脅情報平台和行為分析解決方案代表著市場擴張的機會。

依賴獲取高品質數據

ML 效能在很大程度上依賴公正、多樣化和高品質的數據。然而，許多行業缺乏合適或高品質的資料集，限制了人工智慧模型的有效性。資料碎片化、來源不一致以及隱私限制是導致資料收集困難的一些因素。此外，為了保持準確性，機器學習模型必須經常使用新資料進行更新，但資料存取通常受到專有或監管限制。如果沒有足夠可靠的資料集，機器學習模型可能會產生過時、錯誤或誤導性的見解，從而降低其對業務的整體效用。

COVID-19的影響：

隨著企業和組織尋求創造性的方法來應對疫情的影響，COVID-19 疫情顯著加速了許多產業對機器學習 (ML) 的採用。數據分析、預測模型和即時決策需要更先進的人工智慧驅動系統，對遠端工作、數位服務和自動化的依賴性不斷增加就證明了這一點。在醫療領域，機器學習在開發疫苗、診斷設備和改善患者照護發揮了關鍵作用。金融服務、供應鏈管理和電子商務也在使用機器學習 (ML) 來檢測詐欺、預測需求並提供個人化的客戶體驗。此外，疫情也凸顯了演算法偏見、資料隱私問題以及熟練的機器學習專家短缺等問題。

預測分析領域預計將成為預測期內最大的領域

預計預測期內預測分析部分將佔據最大的市場佔有率。這一領域對零售、醫療保健和金融等許多行業都至關重要，因為它使用機器學習演算法來預測未來結果並分析歷史數據。預測分析使企業能夠預測趨勢、客戶行為和業務需求，支援更好的決策、流程最佳化和客戶體驗。需求預測、風險管理和庫存最佳化只是其眾多用途的一部分。此外，預測分析主導機器學習市場的主要原因之一是對數據主導洞察力的日益依賴以及跨行業產生的數據量。

預計醫療保健領域在預測期內將以最高的複合年成長率成長。

機器學習正在透過實現個人化醫療、提高診斷準確性和改善患者治療效果來徹底改變醫療保健。 ML 分析大量醫療數據的能力有助於早期疾病檢測、治療最佳化和藥物發現。此外，機器學習演算法也被用於醫療保健自動化、預測分析和醫學成像，以提高效率並顯著降低成本。隨著醫療保健提供者繼續擁抱數位轉型，未來幾年對先進機器學習解決方案的需求可能會大幅增加，以解決棘手問題並加強患者照護。

佔比最大的地區：

預計北美地區將在預測期內佔據最大的市場佔有率。重要科技公司的存在、先進的基礎設施以及大量的研發支出是這項優勢的主要因素。大量新興企業和老字型大小企業正在醫療保健、金融、IT 和汽車等行業實施 ML 解決方案，使該地區成為 AI 和 ML 創新的中心。機器學習的採用率不斷提高也為政府專案和人工智慧技術帶來了更多資金籌措。此外，北美憑藉其強大的數據收集和處理能力以及高度的技術意識，引領全球機器學習市場。

複合年成長率最高的地區：

預計亞太地區在預測期內的複合年成長率最高。這一成長是由快速的數位轉型、人工智慧技術的日益普及以及對數據分析的大規模投資所推動的。中國、印度和日本等國家正努力將機器學習 (ML) 融入製造業、金融和醫療保健等各個產業。此外，該地區龐大的人口、不斷成長的智慧型手機普及率以及不斷擴大的網路連接進一步刺激了對機器學習解決方案的需求。亞太地區是機器學習應用成長最快的地區，這得益於政府推廣智慧技術的措施以及國際科技公司的崛起。

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  • 地理細分
    • 根據客戶興趣對主要國家市場進行估計、預測和複合年成長率（註：基於可行性檢查）
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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 次級研究資訊來源
    • 先決條件

第3章市場走勢分析

  • 驅動程式
  • 限制因素
  • 機會
  • 威脅
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第5章 全球機器學習（ML）市場（按組件）

  • 硬體
  • 軟體
  • 服務

第6章 全球機器學習（ML）市場（依公司規模）

  • 中小企業
  • 大型企業

第7章 全球機器學習（ML）市場（按部署）

  • 雲
  • 本地

8. 全球機器學習（ML）市場（按應用）

  • 預測分析
  • 自然語言處理（NLP）
  • 影像識別
  • 詐欺偵測
  • 其他應用

第9章 全球機器學習（ML）市場（按最終用戶）

  • 衛生保健
  • 銀行、金融服務和保險（BFSI）
  • 資訊科技/通訊
  • 廣告和媒體
  • 汽車與運輸
  • 農業
  • 能源與公共產業
  • 製造業
  • 其他最終用戶

第10章 全球機器學習（ML）市場（按地區）

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • 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.