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市場調查報告書
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2124803

自動化機器學習：市場佔有率分析、產業趨勢與統計、成長預測（2026-2031）

Automated Machine Learning - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 110 Pages | 商品交期: 2-3個工作天內

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簡介目錄

據 Mordor Intelligence 稱，2025 年自動化機器學習市場價值為 25.9 億美元，預計到 2031 年將達到 211.9 億美元，而 2026 年為 36.8 億美元，預測期（2026-2031 年）的複合年成長率為 41.96%。

自動化機器學習市場-IMG1

本報告按解決方案（本地部署和雲端部署）、自動化類型（資料處理、特徵工程、建模、視覺化）、組織規模（大型企業和中小企業）、最終用戶（銀行、金融服務和保險 (BFSI)、零售和電子商務及其他）以及地區對行業進行細分。所有細分市場的市場規模和預測均以美元 (USD) 為單位。

全球自動化機器學習市場趨勢與洞察

對高效詐欺偵測模型的需求日益成長

金融機構正從靜態規則集轉向基於自動化機器學習（AutoML）的詐欺偵測系統，這些系統能夠從即時交易流程中學習，從而減少誤報並提高追回率。保險公司預計，到2032年，隨著自動化模型分析結構化和非結構化資料以識別可疑索賠，成本將節省800億至1600億美元。內建的自然語言處理功能使該平台能夠分析客服中心通話記錄和社交媒體訊號，為負責人提供詳細的風險評估背景資訊。隨著金融監理機構收緊資訊揭露標準，提供將可解釋指標與每個預測關聯起來的儀錶板的供應商正在收取更高的費用。預計到2026年，這種協同效應將使複合年成長率（CAGR）維持在8.2%。

對智慧業務流程日益成長的需求

隨著企業從基於規則的機器人技術轉向自適應最佳化，它們正在將 AutoML 整合到製造、零售和醫療保健的工作商品行銷引擎的先導計畫顯示，銷售額成長了 22.7%。 Oracle 的臨床數位助理在醫療保健領域展現出顯著優勢，可將醫生建立文件的時間減少高達 40%，從而騰出更多時間用於患者照護。預計到 2020 年代中期，分析、工作流程編配和低程式碼建模工具的整合將貢獻 7.1% 的成長。

企業採納延遲和文化差異

傳統流程、規避風險的經營團隊以及員工對失業的擔憂正在減緩自動化機器學習（AutoML）的普及，導致市場成長動能下降4.8%。許多亞洲銀行仍然依賴人工洗錢防制審核，因為老舊的核心系統使得資料整合變得複雜。南非的中小型製造業指出，框架不明確且缺乏經營團隊支持是人工智慧專案的主要限制因素，導致引進週期超出預期。成功的轉型需要結合培訓、變革管理方案以及將人工智慧成果與員工績效指標掛鉤的獎勵。

細分市場分析

預計到 2025 年，雲端平台將佔總收入的 63.42%，並且到 2031 年，該細分市場預計將以 43.72% 的複合年成長率成長，這一成長軌跡凸顯了共用基礎設施的成本優勢。隨著超大規模資料中心業者整合專用加速器和無伺服器訓練管道，雲端採用中的自動化機器學習市場預計將會擴大。持續的功能發布、企業級安全認證和付費使用制，使其對那些優先考慮敏捷性而非硬體管理的企業極具吸引力。 AWS 的模型市場 Bedrock 提供超過 100 個基礎模型和特定任務模型，讓客戶無需擁有 GPU 即可評估演算法，從而縮短實驗週期。

在金融、國防和公共部門，由於資料居住要求禁止外部託管，本地部署仍然普遍存在。然而，隨著敏感運算技術的出現，在公共雲端環境中進行安全處理成為可能，本地部署的佔有率正在下降。為了達到預期的延遲目標，混合模式正在興起，即在雲端進行訓練，在邊緣設備上執行推理。邊緣原生服務支援工廠和零售店的離線運營，即使在網路連線中斷的情況下也能確保業務永續營運。

預計到2025年，建模自動化將佔總營收的40.35%，而特徵工程43.11%的複合年成長率顯示人工智慧正朝著以資料為中心的方向發展。由於缺乏穩健的變數構建，結構化資料專案往往難以成功，因此自動化機器學習在特徵工程領域的市場佔有率正在不斷擴大。大規模語言模型（LLM）現在能夠幫助將原始欄位對應到領域感知特徵，從而自動完成以前需要專業知識才能完成的語義連接和文字嵌入。

視覺化和自動化資料處理透過將簡單語言問題轉化為 SQL查詢和互動式圖表，推動了相關技術的廣泛應用。結合演化演算法和 LLM 提示的研究已成功縮短了計算時間，同時提高了基準資料集的預測準確率。醫療保健和金融行業的用戶受益最大，因為特定領域的本體已整合到特徵管道中，無需人工干預即可滿足審計要求。

區域分析

2025年，北美地區佔全球收入的45.38%，這主要得益於其密集的雲端基礎設施部署、成熟的創業投資生態系統以及銀行業和科技業的高採用率。隨著受監管產業將核心工作負載遷移到符合FedRAMP標準的區域，Oracle的雲端基礎設施收入在2025會計年度成長了52%。 2024年，創業投資人完成了200多輪與自動化機器學習（AutoML）相關的資金籌措，從而培育出一批充滿活力的新創企業，加速產品創新。

亞太地區預計將成為成長最強勁的地區，到2031年複合年成長率將達到44.63%，這得益於世界各國政府紛紛部署國家人工智慧戰略。在日本，智慧城市試點計畫、重工業預測性維護計畫以及支援互動式語言的對話式智慧體的推動下，日本的人工智慧經濟規模預計將從2027年的45億美元成長到73億美元。中國正在鞏固其在研發和商業化領域的領先地位，在44個關鍵技術領域中的37個領域的專利申請量位居榜首。東南亞的製造商正在採用自動化機器學習（AutoML）技術來最佳化產量，以應對不斷上漲的人事費用和供應鏈波動的影響。

歐洲的情況錯綜複雜。 GDPR 和即將訂定的人工智慧法規將引入更嚴格的管治，延長銷售週期，但最終將有利於那些內建透明度管理功能的平台。儘管預計到 2024 年，該地區的人工智慧採用率將加倍，達到 13%，但許多公司將技術開發外包這一事實，為託管式 AutoML 服務提供了機會。各國經濟復甦基金已撥款數十億歐元用於數位轉型項目，包括需要自動化建模引擎的醫療保健數據空間。

在中東，各國正大力投資以實現經濟多元化。沙烏地阿拉伯在其「2030願景」中，已撥款1000億美元用於人工智慧和數位基礎設施建設，並為規劃中的6吉瓦資料中心走廊提供額外資金。阿拉伯聯合大公國的目標是透過其「2031人工智慧策略」將聯邦政府服務成本降低50%，該策略正推動採用AutoML平台來實現公民服務的自動化。在南美洲，巴西正受益於其國家人工智慧戰略，該戰略資助葡萄牙語語言模型的開發和高效能運算（HPC）能力的升級。非洲正成為新興的前線陣地，40%的受訪機構正在試行人工智慧，而雲端託管的AutoML正在降低本地運算資源匱乏地區採用人工智慧的門檻。

其他好處

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章：引言

  • 研究假設和市場定義
  • 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

  • 市場概覽
  • 市場促進因素
    • 對高效詐欺偵測模型的需求日益成長
    • 對智慧業務流程日益成長的需求
    • 企業「雲端優先」機器學習策略
    • 熟練的資料科學人員短缺
    • 用於裝置上推理的邊緣原生 AutoML（報告不足）
    • 監理機關為提高模型可解釋性所做的努力（漏報）
  • 市場限制因素
    • 企業採納延遲和文化差異
    • 雲端工作流程中的資料安全與隱私問題
    • 與演算法偏差相關的合規成本（未充分報告）
    • AutoML在長期時間序列資料中的準確性限制（漏報）
  • 價值供應鏈分析
  • 監理情勢
  • 技術展望
  • 波特五力分析
  • 主要宏觀經濟趨勢的影響

第5章 市場規模與成長預測

  • 透過解決方案
    • 現場
    • 雲
  • 按自動化類型
    • 資料處理
    • 特徵工程
    • 造型
    • 視覺化
  • 按組織規模
    • 大公司
    • 中小企業
  • 最終用戶
    • BFSI
    • 零售與電子商務
    • 衛生保健
    • 製造業
    • 其他
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 歐洲
      • 英國
      • 德國
      • 法國
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 韓國
      • 亞太其他地區
    • 中東和非洲
      • 阿拉伯聯合大公國
      • 沙烏地阿拉伯
      • 南非
      • 其他中東和非洲
    • 南美洲
      • 阿根廷
      • 巴西
      • 南美洲其他地區

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • Amazon Web Services, Inc.
    • Alphabet Inc.
    • Microsoft Corporation
    • International Business Machines Corporation
    • DataRobot, Inc.
    • H2O.ai, Inc.
    • Dataiku, Inc.
    • SAS Institute Inc.
    • dotData, Inc.
    • Aible, Inc.
    • Oracle Corporation
    • SAP SE
    • Alteryx, Inc.
    • RapidMiner, Inc.
    • KNIME AG
    • BigML, Inc.
    • TIBCO Software Inc.
    • Databricks, Inc.
    • Hewlett Packard Enterprise Company

第7章 市場機會與未來展望

簡介目錄
Product Code: 90609

According to Mordor Intelligence, the automated machine learning market size was valued at USD 2.59 billion in 2025 and estimated to grow from USD 3.68 billion in 2026 to reach USD 21.19 billion by 2031, at a CAGR of 41.96% during the forecast period (2026-2031).

Automated Machine Learning - Market - IMG1

This report Segments the Industry Into Solution (On-Premise and Cloud), Automation Type (Data Processing, Feature Engineering, Modeling, and Visualization), Organization Size (Large Enterprises and Small and Medium Enterprises [SMEs]), End User (BFSI, Retail and E-Commerce, and More) and Geography. The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Global Automated Machine Learning Market Trends and Insights

Rising Demand for Efficient Fraud-Detection Models

Financial institutions are moving from static rule sets to AutoML-based fraud systems that learn from real-time transaction flows, cutting false positives and improving recovery rates. Insurers expect savings of USD 80-160 billion by 2032 as automated models mine structured and unstructured data for suspicious claims. Built-in natural-language processing allows platforms to digest call-centre transcripts and social-media signals, giving underwriters granular context for risk decisions. Vendors that deliver dashboards linking explanatory metrics to each prediction command price premiums because finance regulators are tightening disclosure standards. The net effect sustains an 8.2% uplift on forecast CAGR through 2026.

Increasing Need for Intelligent Business Processes

Enterprises are embedding AutoML inside manufacturing, retail, and healthcare workflows to move beyond rule-based robotics toward adaptive optimisation. Sensor-driven predictive maintenance trims unplanned downtime by up to 30% and improves overall equipment effectiveness across semiconductor fabrication lines. Retailers apply AutoML to demand planning and dynamic pricing, with pilots showing 22.7% revenue lifts when AI-generated insights feed merchandising engines. Oracle's Clinical Digital Assistant illustrates healthcare gains, reducing physician documentation time by as much as 40% and freeing capacity for patient care. The convergence of analytics, workflow orchestration, and low-code modelling tools propels a 7.1% contribution to growth through mid-decade.

Slow Enterprise Adoption and Culture Gap

Legacy processes, risk-averse leadership, and workforce apprehension about job displacement slow AutoML rollouts, reducing market momentum by 4.8%. Many Asian banks still rely on manual anti-money-laundering reviews because their aged core systems complicate data integration. Small manufacturing firms in South Africa cite unclear frameworks and limited managerial sponsorship as primary impediments to AI projects, pushing deployment cycles beyond initial forecasts. Successful transformations combine training, change-management programs, and targeted incentives that align AI outcomes with employee performance metrics.

Other drivers and restraints analyzed in the detailed report include:

  1. Cloud-First ML Strategy of Enterprises
  2. Shortage of Skilled Data-Science Labour
  3. Data-Security and Privacy Concerns in Cloud Workflows

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Cloud platforms generated 63.42% of revenue in 2025, and the segment is on track for 43.72% CAGR through 2031, a trajectory that validates the cost advantages of shared infrastructure. The automated machine learning market size for cloud deployments is projected to widen as hyperscalers integrate dedicated accelerators and serverless training pipelines. Continuous feature releases, enterprise-grade security certifications, and usage-based billing appeal to organisations seeking agility over hardware control. Bedrock, AWS's model marketplace, lists more than 100 foundational and task-specific models, letting clients evaluate algorithms without owning GPUs, which compresses experimentation cycles.

On-premises deployments persist in finance, defence, and public sectors where data-residency mandates prohibit external hosting. Their share, however, is eroding as confidential-computing techniques allow secure processing in public-cloud environments. Hybrid patterns have emerged in which training occurs in the cloud while inference runs on edge devices to meet latency targets. Edge-native offerings enable offline operation for factories and retail outlets, ensuring business continuity when connectivity drops.

Modeling automation retained 40.35% of 2025 revenue yet feature engineering's 43.11% CAGR signals a shift toward data-centric AI. The automated machine learning market share for feature automation is expanding because structured-data projects often fail without robust variable construction. Large language models now assist in mapping raw fields to domain-ready features, automating semantic joins and text embeddings that previously demanded specialist knowledge.

Visualization and data-processing automation support wider adoption by translating plain-language questions into SQL queries and interactive charts. Research combining evolutionary algorithms with LLM prompts has cut computation time while improving predictive lift on benchmark datasets. Healthcare and finance users benefit most, as domain-specific ontologies are embedded into feature pipelines, satisfying auditing requirements without manual intervention.

Complete Report Scope:

  • By Solution
    • On-premise
    • Cloud
  • By Automation Type
    • Data Processing
    • Feature Engineering
    • Modeling
    • Visualization
  • By Organization Size
    • Large Enterprises
    • Small and Medium Enterprises (SMEs)
  • By End-user
    • BFSI
    • Retail and E-commerce
    • Healthcare
    • Manufacturing
    • Other End-users
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • Europe
      • United Kingdom
      • Germany
      • France
      • Rest of Europe
    • Asia-Pacific
      • China
      • Japan
      • South Korea
      • Rest of Asia-Pacific
    • Middle East and Africa
      • United Arab Emirates
      • Saudi Arabia
      • South Africa
      • Rest of Middle East and Africa
    • South America
      • Argentina
      • Brazil
      • Rest of South America

Geography Analysis

North America generated 45.38% of global revenue in 2025 on the back of dense cloud-infrastructure footprints, a mature venture-capital ecosystem, and high adoption in banking and technology sectors. Oracle's cloud-infrastructure revenue rose 52% in fiscal 2025 as regulated industries moved core workloads to its FedRAMP-compliant regions. Venture investors closed more than 200 AutoML-related funding rounds in 2024, feeding a vibrant start-up pipeline that accelerates product innovation.

Asia Pacific records the strongest trajectory with 44.63% CAGR through 2031 as governments deploy national AI strategies. Japan's AI economy is projected to expand from USD 4.5 billion to USD 7.3 billion by 2027, driven by smart-city pilots, predictive maintenance programs in heavy industry, and local-language conversational agents. China leads in patent publications for 37 of 44 critical technologies, affirming its status as a powerhouse for both research and commercial implementation. Southeast Asian manufacturers adopt AutoML for yield optimisation to offset rising labour costs and supply-chain volatility.

Europe presents a mixed environment. The GDPR and forthcoming AI Act introduce strict governance that elongates sales cycles but ultimately favours platforms with embedded transparency controls. The region's AI adoption doubled to 13% by 2024, yet many firms outsource technical builds, creating fertile ground for managed AutoML services. National recovery funds earmark billions of euros for digital-transformation projects, including health-data spaces that require automated modelling engines.

The Middle East pursues headline investments to diversify economies. Saudi Arabia has earmarked USD 100 billion for AI and digital infrastructure under Vision 2030, with further capital allocated to a planned 6-gigawatt data-centre corridor. The United Arab Emirates expects its AI Strategy 2031 to cut federal-service costs by 50%, driving procurement of AutoML platforms that automate citizen services. South America benefits from Brazil's national AI strategy, which funds Portuguese-language models and HPC upgrades. Africa is an emerging frontier; 40% of surveyed institutions are piloting AI, and cloud-hosted AutoML lowers the barrier where local compute resources remain scarce.

  1. Amazon Web Services, Inc.
  2. Alphabet Inc.
  3. Microsoft Corporation
  4. International Business Machines Corporation
  5. DataRobot, Inc.
  6. H2O.ai, Inc.
  7. Dataiku, Inc.
  8. SAS Institute Inc.
  9. dotData, Inc.
  10. Aible, Inc.
  11. Oracle Corporation
  12. SAP SE
  13. Alteryx, Inc.
  14. RapidMiner, Inc.
  15. KNIME AG
  16. BigML, Inc.
  17. TIBCO Software Inc.
  18. Databricks, Inc.
  19. Hewlett Packard Enterprise Company

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Rising demand for efficient fraud-detection models
    • 4.2.2 Increasing need for intelligent business processes
    • 4.2.3 Cloud-first ML strategy of enterprises
    • 4.2.4 Shortage of skilled data-science labour
    • 4.2.5 Edge-native AutoML for on-device inference (under-reported)
    • 4.2.6 Regulatory push for model explainability (under-reported)
  • 4.3 Market Restraints
    • 4.3.1 Slow enterprise adoption and culture gap
    • 4.3.2 Data-security and privacy concerns in cloud workflows
    • 4.3.3 Algorithmic bias compliance costs (under-reported)
    • 4.3.4 Limited AutoML accuracy on long-horizon time-series (under-reported)
  • 4.4 Value/Supply-Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Threat of New Entrants
    • 4.7.2 Bargaining Power of Buyers
    • 4.7.3 Bargaining Power of Suppliers
    • 4.7.4 Threat of Substitutes
    • 4.7.5 Intensity of Competitive Rivalry
  • 4.8 Impact of Key Macroeconomic Trends

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Solution
    • 5.1.1 On-premise
    • 5.1.2 Cloud
  • 5.2 By Automation Type
    • 5.2.1 Data Processing
    • 5.2.2 Feature Engineering
    • 5.2.3 Modeling
    • 5.2.4 Visualization
  • 5.3 By Organization Size
    • 5.3.1 Large Enterprises
    • 5.3.2 Small and Medium Enterprises (SMEs)
  • 5.4 By End-user
    • 5.4.1 BFSI
    • 5.4.2 Retail and E-commerce
    • 5.4.3 Healthcare
    • 5.4.4 Manufacturing
    • 5.4.5 Other End-users
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 Europe
      • 5.5.2.1 United Kingdom
      • 5.5.2.2 Germany
      • 5.5.2.3 France
      • 5.5.2.4 Rest of Europe
    • 5.5.3 Asia-Pacific
      • 5.5.3.1 China
      • 5.5.3.2 Japan
      • 5.5.3.3 South Korea
      • 5.5.3.4 Rest of Asia-Pacific
    • 5.5.4 Middle East and Africa
      • 5.5.4.1 United Arab Emirates
      • 5.5.4.2 Saudi Arabia
      • 5.5.4.3 South Africa
      • 5.5.4.4 Rest of Middle East and Africa
    • 5.5.5 South America
      • 5.5.5.1 Argentina
      • 5.5.5.2 Brazil
      • 5.5.5.3 Rest of South America

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global level Overview, Market level overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
    • 6.4.1 Amazon Web Services, Inc.
    • 6.4.2 Alphabet Inc.
    • 6.4.3 Microsoft Corporation
    • 6.4.4 International Business Machines Corporation
    • 6.4.5 DataRobot, Inc.
    • 6.4.6 H2O.ai, Inc.
    • 6.4.7 Dataiku, Inc.
    • 6.4.8 SAS Institute Inc.
    • 6.4.9 dotData, Inc.
    • 6.4.10 Aible, Inc.
    • 6.4.11 Oracle Corporation
    • 6.4.12 SAP SE
    • 6.4.13 Alteryx, Inc.
    • 6.4.14 RapidMiner, Inc.
    • 6.4.15 KNIME AG
    • 6.4.16 BigML, Inc.
    • 6.4.17 TIBCO Software Inc.
    • 6.4.18 Databricks, Inc.
    • 6.4.19 Hewlett Packard Enterprise Company

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-space and Unmet-need Assessment