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市場調查報告書
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1878416

自動機器學習 (AUTOML) 市場 - 2025 年至 2030 年預測

Automated Machine Learning (AUTOML) Market - Forecasts from 2025 to 2030
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 148 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

預計自動化機器學習 (AutoML) 市場將從 2025 年的 19.33 億美元成長到 2030 年的 113.06 億美元，複合年成長率為 42.37%。

自動化機器學習 (AutoML) 市場的特點是快速採用各種技術，這些技術能夠自動化建置、最佳化和部署機器學習模型的端到端流程。透過使用人工智慧處理特徵工程、演算法選擇和超參數調優等複雜任務，AutoML 平台顯著降低了進階資料分析的准入門檻。這使得內部資料科學專業知識有限的組織也能開發和運行預測模型，從而普及了人工智慧驅動的洞察。技術趨勢與不斷變化的業務需求的融合推動了市場擴張，使 AutoML 成為企業數位轉型中的關鍵工具。

主要市場成長要素

推動 AutoML 市場發展的核心動力是人工智慧普及化的整體趨勢以及對低程式碼/無程式碼解決方案日益成長的需求。傳統上，對高度專業化的資料科學家的依賴給許多組織造成了嚴重的人才瓶頸。 AutoML 透過提供直覺的介面直接解決了這個難題，使幾乎沒有機器學習經驗的業務分析師、領域專家和軟體開發人員也能建立強大的模型。這種轉變將使預測分析惠及更廣泛的人才，加速人工智慧融入各種業務職能，並推動整個組織採用人工智慧技術。

雲端基礎機器學習平台的日益普及進一步加速了市場成長。主流雲端服務供應商正將 AutoML 功能直接整合到其服務組合中，提供可擴展的運算能力、整合的資料管道和託管基礎設施。這種雲端原生方法無需對本地硬體進行大量前期投資，並簡化了模型部署和管理。 AutoML 與廣泛的雲端生態系無縫整合，使各種規模的企業都能更便捷、更有效率地使用高階分析功能。

此外，企業產生的數據量呈指數級成長，這推動了對高效分析工具的需求。各行各業的組織都意識到，需要從數據中提取可執行的洞察，以保持競爭優勢。 AutoML平台透過簡化模型開發生命週期來滿足此需求，從而能夠快速建立和迭代預測模型，應用於客戶細分、銷售預測和營運最佳化等場景。快速從資料資產中獲取價值的能力是推動企業投資AutoML技術的關鍵因素。

市場動態與限制因素

儘管成長要素強勁，但市場也面臨許多不利因素。採用和整合 AutoML 平台的初始成本可能相當高昂，尤其對於中小企業而言。這些成本不僅包括軟體許可，還包括雲端基礎設施、數據管道建置、系統整合，以及員工再培訓和外部諮詢費用。這種財務障礙可能會阻礙成本敏感型企業採用此平台。

另一個挑戰是許多現成的 AutoML 解決方案固有的客製化能力有限。雖然這些平台擅長自動化標準工作流程，但對於具有高度特定或獨特需求的公司而言，它們的靈活性可能不足。將這些平台整合到複雜的傳統 IT 環境中，或針對特定用例進行客製化，可能會造成重大的維運障礙，從而限制了效用。

市場區隔與區域分析

  • 從市場區隔來看，服務供應商格局主要由成熟的科技巨頭和敏捷的Start-Ups主導。大型雲端服務供應商提供全面的雲端整合式自動化機器學習（AutoML）平台，充分利用其廣泛的人工智慧研究和全球基礎設施。同時，專注於開發高度易用的無程式碼解決方案的Start-Ups，主要面向中小企業和企業用戶，進一步擴大了其市場覆蓋範圍。
  • 從應用角度來看，詐欺偵測是一個重要且快速發展的領域。 AutoML能夠快速處理大量交易資料、識別異常模式並持續改進檢測模型，這在銀行、金融和保險（BFSI）以及電子商務領域尤其重要。網路金融詐騙的持續挑戰不斷推動該應用領域的創新和應用。
  • 從區域來看，北美憑藉著成熟的人工智慧生態系統、主要技術供應商的集中以及跨產業對高級分析技術的早期應用，在自動化機器學習（AutoML）市場保持主導地位。與此同時，亞太地區正經歷最快的成長，這得益於積極的數位轉型、支持人工智慧發展的政府政策以及蓬勃發展的數位經濟。歐洲是一個穩健且監管完善的市場，其應用與嚴格的資料保護法律相平衡。而南美和中東等地區仍處於市場發展的早期階段，但在特定國家舉措和行業重點的推動下，這些地區的市場發展正在加速推進。
  • 競爭環境
  • 競爭格局正逐漸向IBM、微軟、亞馬遜網路服務（AWS）和谷歌等大型科技公司集中，這些公司利用其廣泛的雲端和人工智慧產品組合提供整合的AutoML服務。除了這些老牌企業之外，Databricks、Akkio Inc.和Obviously AI, Inc.等專業公司也憑藉其方便用戶使用的介面和專業解決方案參與競爭。市場走向正受到持續產品改進的影響，尤其是低程式碼體驗的最佳化以及AutoML與更廣泛的資料科學和分析平台的整合。

本報告的主要優勢：

  • 深入分析：提供對主要和新興地區的深入市場洞察，重點關注客戶群、政府政策和社會經濟因素、消費者偏好、垂直行業和其他細分市場。
  • 競爭格局：了解全球主要企業的策略舉措，並了解透過正確的策略實現市場滲透的潛力。
  • 市場促進因素與未來趨勢：探索推動市場的動態因素和關鍵趨勢及其對未來市場發展的影響。
  • 可操作的建議：利用這些見解，在動態環境中製定策略決策，發展新的商業機會和收入來源。
  • 受眾廣泛：適用於Start-Ups、研究機構、顧問公司、中小企業和大型企業，且經濟實惠。
  • 以下是一些公司如何使用這份報告的範例
  • 產業與市場分析、機會評估、產品需求預測、打入市場策略、地理擴張、資本投資決策、法規結構及影響、新產品開發、競爭情報

報告範圍：

  • 2022年至2024年的歷史數據和2025年至2030年的預測數據
  • 成長機會、挑戰、供應鏈前景、法規結構與趨勢分析
  • 競爭定位、策略和市場佔有率分析
  • 按業務板塊和地區分類的收入成長和預測評估，包括國家/地區
  • 公司概況（策略、產品、財務資訊、關鍵發展等）
  • AutoML市場按以下方式進行細分與分析：
  • 自動機器學習 (AutoML) 市場按產品/服務分類
  • 解決方案
  • 服務
  • 按部署類型分類的自動化機器學習 (AUTOML) 市場
  • 雲
  • 本地部署
  • 按公司規模分類的自動化機器學習 (AUTOML) 市場
  • 中小企業
  • 主要企業
  • 按應用分類的自動化機器學習 (AUTOML) 市場
  • 詐欺偵測
  • AML偵測
  • 行銷與銷售管理
  • 資料處理
  • 特徵工程
  • 其他
  • 按最終用戶分類的自動化機器學習 (AUTOML) 市場
  • BFSI
  • 醫療保健
  • 零售與電子商務
  • 製造業
  • 資訊科技/通訊
  • 其他
  • 按地區分類的自動化機器學習 (AUTOML) 市場
  • 北美洲
  • 美國
  • 加拿大
  • 墨西哥
  • 南美洲
  • 巴西
  • 阿根廷
  • 其他
  • 歐洲
  • 德國
  • 法國
  • 英國
  • 西班牙
  • 其他
  • 中東和非洲
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 以色列
  • 其他
  • 亞太地區
  • 中國
  • 印度
  • 日本
  • 韓國
  • 其他

目錄

第1章執行摘要

第2章 市場概覽

  • 市場概覽
  • 市場定義
  • 調查範圍

第2章 4. 市場區隔

第3章 商業情境

  • 市場促進因素
  • 市場限制
  • 市場機遇
  • 波特五力分析
  • 產業價值鏈分析
  • 政策與法規
  • 策略建議

第4章 技術展望

5. 按供應商分類的自動化機器學習 (AUTOML) 市場

  • 介紹
  • 開放原始碼
  • Start-Ups公司
  • 科技巨頭

6. 按發展階段分類的自動化機器學習 (AUTOML) 市場

  • 介紹
  • 雲端基礎的
  • 本地部署

7. 按應用分類的汽車機器學習市場

  • 介紹
  • 詐欺偵測
  • AML偵測
  • 定價
  • 行銷與銷售管理
  • 其他

8. 按地區分類的自動化機器學習 (AUTOML) 市場

  • 介紹
  • 北美洲
    • 按提供者
    • 透過使用
    • 按國家/地區
      • 美國
      • 加拿大
      • 墨西哥
  • 南美洲
    • 按提供者
    • 透過使用
    • 按國家/地區
      • 巴西
      • 阿根廷
      • 其他
  • 歐洲
    • 按提供者
    • 透過使用
    • 按國家/地區
      • 英國
      • 德國
      • 法國
      • 西班牙
      • 其他
  • 中東和非洲
    • 按提供者
    • 透過使用
    • 按國家/地區
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 以色列
      • 其他
  • 亞太地區
    • 按提供者
    • 透過使用
    • 按國家/地區
      • 日本
      • 中國
      • 印度
      • 韓國
      • 印尼
      • 泰國
      • 其他

第9章 競爭格局與分析

  • 主要企業和策略分析
  • 市佔率分析
  • 合併、收購、協議和合作
  • 競爭對手儀錶板

第10章：公司簡介

  • IBM
  • Microsoft Corporation
  • Amazon Web Services
  • Oracle
  • Alphabet Inc.（Google）
  • Databricks
  • Qlik
  • Akkio Inc.
  • Obviously AI, Inc.

第11章調查方法

簡介目錄
Product Code: KSI061615199

Automated Machine Learning (AUTOML) Market, at a 42.37% CAGR, is projected to increase from USD 1.933 billion in 2025 to USD 11.306 billion by 2030.

The Automated Machine Learning (AutoML) market is characterized by the rapid adoption of technologies designed to automate the end-to-end process of building, optimizing, and deploying machine learning models. By leveraging artificial intelligence to handle complex tasks such as feature engineering, algorithm selection, and hyperparameter tuning, AutoML platforms significantly lower the barrier to entry for advanced data analytics. This enables organizations with limited in-house data science expertise to develop and operationalize predictive models, thereby democratizing access to AI-driven insights. The market's expansion is underpinned by a convergence of technological trends and evolving business needs, positioning AutoML as a critical tool for enterprise digital transformation.

Primary Market Growth Drivers

A central force propelling the AutoML market is the overarching trend toward AI democratization and the rising demand for low-code and no-code solutions. The historical reliance on highly specialized data scientists created a significant talent bottleneck for many organizations. AutoML directly addresses this constraint by providing intuitive interfaces that allow business analysts, domain experts, and software developers with minimal machine learning training to construct robust models. This shift empowers a broader range of personnel to leverage predictive analytics, accelerating the integration of AI into diverse business functions and driving widespread organizational adoption.

The increasing adoption of cloud-based machine learning platforms further catalyzes market growth. Leading cloud service providers have embedded AutoML capabilities directly into their service portfolios, offering scalable computing power, integrated data pipelines, and managed infrastructure. This cloud-native approach eliminates the need for substantial upfront investment in on-premises hardware and simplifies the deployment and management of models. The seamless integration of AutoML within broader cloud ecosystems makes advanced analytics more accessible and operationally efficient for enterprises of all sizes.

Furthermore, the escalating volume of data generated by businesses is creating an imperative for efficient analytical tools. Organizations across sectors are recognizing the need to extract actionable insights from their data to maintain a competitive edge. AutoML platforms meet this need by streamlining the model development lifecycle, enabling companies to rapidly build and iterate on predictive models for applications such as customer segmentation, sales forecasting, and operational optimization. The ability to quickly derive value from data assets is a key factor motivating investment in AutoML technologies.

Market Dynamics and Constraints

Despite strong growth drivers, the market faces certain headwinds. The initial implementation and integration costs associated with AutoML platforms can be substantial, particularly for small and medium-sized enterprises (SMEs). These costs extend beyond software licensing to encompass cloud infrastructure, data pipeline configuration, system integration, and potential expenses for staff retraining or external consultants. This financial barrier can inhibit adoption in cost-sensitive environments.

Another challenge is the inherent limitation in customization offered by many out-of-the-box AutoML solutions. While these platforms excel at automating standard workflows, businesses with highly specific or unique requirements may find the solutions insufficiently flexible. Integrating these platforms into complex, legacy IT environments and tailoring them to specialized use cases can present significant operational hurdles, potentially limiting their utility for certain advanced applications.

Market Segmentation and Regional Analysis

  • In terms of market segmentation, the provider landscape is dominated by established technology giants and agile startups. Major cloud providers offer comprehensive, cloud-integrated AutoML platforms that leverage their extensive AI research and global infrastructure. Concurrently, specialized startups are focusing on developing highly accessible, no-code solutions targeted primarily at SMEs and business users, further expanding the market's reach.
  • From an application perspective, fraud detection represents a significant and growing segment. The ability of AutoML to rapidly process large transaction volumes, identify anomalous patterns, and continuously refine detection models makes it particularly valuable for the BFSI and e-commerce sectors. The persistent challenge of online financial fraud is a sustained driver for innovation and adoption in this application area.
  • Geographically, North America maintains a leading position in the AutoML market, driven by its mature AI ecosystem, the concentration of major technology vendors, and early adoption of advanced analytics across industries. Meanwhile, the Asia-Pacific region is experiencing the most rapid growth, fueled by aggressive digital transformation, supportive government policies for AI development, and a booming digital economy. Europe presents a strong, regulated market where adoption is balanced against stringent data protection laws, while regions such as South America and the Middle East are in earlier but accelerating stages of market development, often focused on specific national initiatives and industrial sectors.
  • Competitive Environment
  • The competitive landscape is consolidated around key technology players, including IBM, Microsoft, Amazon Web Services, and Google, which leverage their vast cloud and AI portfolios to offer integrated AutoML services. These established players are complemented by specialized firms like Databricks, Akkio Inc., and Obviously AI, Inc., which compete through user-friendly interfaces and targeted solutions. The market's direction is being shaped by continuous product enhancements, particularly the refinement of low-code experiences and the ongoing integration of AutoML into broader data science and analytics platforms.

Key Benefits of this Report:

  • Insightful Analysis: Gain detailed market insights covering major as well as emerging geographical regions, focusing on customer segments, government policies and socio-economic factors, consumer preferences, industry verticals, and other sub-segments.
  • Competitive Landscape: Understand the strategic maneuvers employed by key players globally to understand possible market penetration with the correct strategy.
  • Market Drivers & Future Trends: Explore the dynamic factors and pivotal market trends and how they will shape future market developments.
  • Actionable Recommendations: Utilize the insights to exercise strategic decisions to uncover new business streams and revenues in a dynamic environment.
  • Caters to a Wide Audience: Beneficial and cost-effective for startups, research institutions, consultants, SMEs, and large enterprises.
  • What do businesses use our reports for?
  • Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive Intelligence

Report Coverage:

  • Historical data from 2022 to 2024 & forecast data from 2025 to 2030
  • Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, and Trend Analysis
  • Competitive Positioning, Strategies, and Market Share Analysis
  • Revenue Growth and Forecast Assessment of segments and regions including countries
  • Company Profiling (Strategies, Products, Financial Information, and Key Developments among others.
  • The Auto ML Market is segmented and analyzed as follows:
  • AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY OFFERINGS
  • Solutions
  • Services
  • AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY DEPLOYMENT
  • Cloud
  • On-Premise
  • AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY ENTERPRISE SIZE
  • Small & Medium Enterprise (SMEs)
  • Large Enterprise
  • AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY APPLICATION
  • Fraud Detection
  • AML Detection
  • Marketing & Sales Management
  • Data Processing
  • Feature Engineering
  • Others
  • AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY END-USER
  • BFSI
  • Healthcare
  • Retail & E-Commerce
  • Manufacturing
  • IT & Telecommunication
  • Others
  • AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY GEOGRAPHY
  • North America
  • USA
  • Canada
  • Mexico
  • South America
  • Brazil
  • Argentina
  • Others
  • Europe
  • Germany
  • France
  • United Kingdom
  • Spain
  • Others
  • Middle East and Africa
  • Saudi Arabia
  • UAE
  • Israel
  • Others
  • Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Others

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

  • 2.1. Market Overview
  • 2.2. Market Definition
  • 2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE

  • 3.1. Market Drivers
  • 3.2. Market Restraints
  • 3.3. Market Opportunities
  • 3.4. Porter's Five Forces Analysis
  • 3.5. Industry Value Chain Analysis
  • 3.6. Policies and Regulations
  • 3.7. Strategic Recommendations

4. TECHNOLOGICAL OUTLOOK

5. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY PROVIDER

  • 5.1. Introduction
  • 5.2. Open Source
  • 5.3. Startups
  • 5.4. Tech Giants

6. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY DEPLOYMENT

  • 6.1. Introduction
  • 6.2. Cloud-Based
  • 6.3. On-Premises

7. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY APPLICATION

  • 7.1. Introduction
  • 7.2. Fraud Detection
  • 7.3. AML Detection
  • 7.4. Pricing
  • 7.5. Marketing and Sales Management
  • 7.6. Others

8. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY GEOGRAPHY

  • 8.1. Introduction
  • 8.2. North America
    • 8.2.1. By Provider
    • 8.2.2. By Application
    • 8.2.3. By Country
      • 8.2.3.1. United States
      • 8.2.3.2. Canada
      • 8.2.3.3. Mexico
  • 8.3. South America
    • 8.3.1. By Provider
    • 8.3.2. By Application
    • 8.3.3. By Country
      • 8.3.3.1. Brazil
      • 8.3.3.2. Argentina
      • 8.3.3.3. Others
  • 8.4. Europe
    • 8.4.1. By Provider
    • 8.4.2. By Application
    • 8.4.3. By Country
      • 8.4.3.1. United Kingdom
      • 8.4.3.2. Germany
      • 8.4.3.3. France
      • 8.4.3.4. Spain
      • 8.4.3.5. Others
  • 8.5. Middle East & Africa
    • 8.5.1. By Provider
    • 8.5.2. By Application
    • 8.5.3. By Country
      • 8.5.3.1. Saudi Arabia
      • 8.5.3.2. UAE
      • 8.5.3.3. Israel
      • 8.5.3.4. Others
  • 8.6. Asia Pacific
    • 8.6.1. By Provider
    • 8.6.2. By Application
    • 8.6.3. By Country
      • 8.6.3.1. Japan
      • 8.6.3.2. China
      • 8.6.3.3. India
      • 8.6.3.4. South Korea
      • 8.6.3.5. Indonesia
      • 8.6.3.6. Thailand
      • 8.6.3.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

  • 9.1. Major Players and Strategy Analysis
  • 9.2. Market Share Analysis
  • 9.3. Mergers, Acquisitions, Agreements, and Collaborations
  • 9.4. Competitive Dashboard

10. COMPANY PROFILES

  • 10.1. IBM
  • 10.2. Microsoft Corporation
  • 10.3. Amazon Web Services
  • 10.4. Oracle
  • 10.5. Alphabet Inc. (Google)
  • 10.6. Databricks
  • 10.7. Qlik
  • 10.8. Akkio Inc.
  • 10.9. Obviously AI, Inc.

11. RESEARCH METHODOLOGY