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市场调查报告书
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1878416

自动机器学习 (AUTOML) 市场 - 2025 年至 2030 年预测

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个工作天内

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简介目录

预计自动化机器学习 (AutoML) 市场将从 2025 年的 19.33 亿美元成长到 2030 年的 113.06 亿美元，复合年增长率为 42.37%。

自动化机器学习 (AutoML) 市场的特点是快速采用各种技术，这些技术能够自动化建置、最佳化和部署机器学习模型的端到端流程。透过使用人工智慧处理特征工程、演算法选择和超参数调优等复杂任务，AutoML 平台显着降低了进阶资料分析的准入门槛。这使得内部资料科学专业知识有限的组织也能开发和运行预测模型，从而普及了人工智慧驱动的洞察。技术趋势与不断变化的业务需求的融合推动了市场扩张，使 AutoML 成为企业数位转型中的关键工具。

主要市场成长要素

推动 AutoML 市场发展的核心动力是人工智慧普及化的整体趋势以及对低程式码/无程式码解决方案日益增长的需求。传统上，对高度专业化的资料科学家的依赖给许多组织造成了严重的人才瓶颈。 AutoML 透过提供直觉的介面直接解决了这个难题，使几乎没有机器学习经验的业务分析师、领域专家和软体开发人员也能建立强大的模型。这种转变将使预测分析惠及更广泛的人才，加速人工智慧融入各种业务职能，并推动整个组织采用人工智慧技术。

云端基础机器学习平台的日益普及进一步加速了市场成长。主流云端服务供应商正将 AutoML 功能直接整合到其服务组合中，提供可扩展的运算能力、整合的资料管道和託管基础设施。这种云端原生方法无需对本地硬体进行大量前期投资，并简化了模型部署和管理。 AutoML 与广泛的云端生态系无缝集成，使各种规模的企业都能更便捷、更有效率地使用高阶分析功能。

此外，企业产生的数据量呈指数级增长，这推动了对高效分析工具的需求。各行各业的组织都意识到，需要从数据中提取可执行的洞察，以保持竞争优势。 AutoML平台透过简化模型开发生命週期来满足此需求，从而能够快速建立和迭代预测模型，应用于客户细分、销售预测和营运最佳化等场景。快速从资料资产中获取价值的能力是推动企业投资AutoML技术的关键因素。

市场动态与限制因素

儘管成长要素强劲，但市场也面临许多不利因素。采用和整合 AutoML 平台的初始成本可能相当高昂，尤其对于中小企业而言。这些成本不仅包括软体许可，还包括云端基础设施、数据管道建置、系统集成，以及员工再培训和外部咨询费用。这种财务障碍可能会阻碍成本敏感型企业采用此平台。

另一个挑战是许多现成的 AutoML 解决方案固有的客製化能力有限。虽然这些平台擅长自动化标准工作流程，但对于具有高度特定或独特需求的公司而言，它们的灵活性可能不足。将这些平台整合到复杂的传统 IT 环境中，或针对特定用例进行定制，可能会造成重大的维运障碍，从而限制了效用。

市场区隔与区域分析

  • 从市场区隔来看，服务供应商格局主要由成熟的科技巨头和敏捷的Start-Ups主导。大型云端服务供应商提供全面的云端整合式自动化机器学习（AutoML）平台，充分利用其广泛的人工智慧研究和全球基础设施。同时，专注于开发高度易用的无程式码解决方案的Start-Ups，主要面向中小企业和企业用户，进一步扩大了其市场覆盖范围。
  • 从应用角度来看，诈欺侦测是一个重要且快速发展的领域。 AutoML能够快速处理大量交易资料、识别异常模式并持续改进检测模型，这在银行、金融和保险（BFSI）以及电子商务领域尤其重要。网路金融诈骗的持续挑战不断推动该应用领域的创新和应用。
  • 从区域来看，北美凭藉着成熟的人工智慧生态系统、主要技术供应商的集中以及跨产业对高级分析技术的早期应用，在自动化机器学习（AutoML）市场保持主导地位。与此同时，亚太地区正经历最快的成长，这得益于积极的数位转型、支持人工智慧发展的政府政策以及蓬勃发展的数位经济。欧洲是一个稳健且监管完善的市场，其应用与严格的资料保护法律相平衡。而南美和中东等地区仍处于市场发展的早期阶段，但在特定国家倡议和行业重点的推动下，这些地区的市场发展正在加速推进。
  • 竞争环境
  • 竞争格局正逐渐向IBM、微软、亚马逊网路服务（AWS）和谷歌等大型科技公司集中，这些公司利用其广泛的云端和人工智慧产品组合提供整合的AutoML服务。除了这些老牌企业之外，Databricks、Akkio Inc.和Obviously AI, Inc.等专业公司也凭藉其用户友好的介面和专业解决方案参与竞争。市场走向正受到持续产品改进的影响，尤其是低程式码体验的最佳化以及AutoML与更广泛的资料科学和分析平台的整合。

本报告的主要优势：

  • 深入分析：提供对主要和新兴地区的深入市场洞察，重点关注客户群、政府政策和社会经济因素、消费者偏好、垂直行业和其他细分市场。
  • 竞争格局：了解全球主要企业的策略倡议，并了解透过正确的策略实现市场渗透的潜力。
  • 市场驱动因素与未来趋势：探索推动市场的动态因素和关键趋势及其对未来市场发展的影响。
  • 可操作的建议：利用这些见解，在动态环境中製定策略决策，发展新的商业机会和收入来源。
  • 受众广泛：适用于Start-Ups、研究机构、顾问公司、中小企业和大型企业，且经济实惠。
  • 以下是一些公司如何使用这份报告的范例
  • 产业与市场分析、机会评估、产品需求预测、打入市场策略、地理扩张、资本投资决策、法规结构及影响、新产品开发、竞争情报

报告范围：

  • 2022年至2024年的历史数据和2025年至2030年的预测数据
  • 成长机会、挑战、供应链前景、法规结构与趋势分析
  • 竞争定位、策略和市场占有率分析
  • 按业务板块和地区分類的收入成长和预测评估，包括国家/地区
  • 公司概况（策略、产品、财务资讯、关键发展等）
  • AutoML市场按以下方式进行细分与分析：
  • 自动机器学习 (AutoML) 市场按产品/服务分类
  • 解决方案
  • 服务
  • 按部署类型分類的自动化机器学习 (AUTOML) 市场
  • 云
  • 本地部署
  • 按公司规模分類的自动化机器学习 (AUTOML) 市场
  • 中小企业
  • 大公司
  • 按应用分類的自动化机器学习 (AUTOML) 市场
  • 诈欺侦测
  • AML侦测
  • 行销与销售管理
  • 资料处理
  • 特征工程
  • 其他的
  • 按最终用户分類的自动化机器学习 (AUTOML) 市场
  • BFSI
  • 医疗保健
  • 零售与电子商务
  • 製造业
  • 资讯科技/通讯
  • 其他的
  • 按地区分類的自动化机器学习 (AUTOML) 市场
  • 北美洲
  • 美国
  • 加拿大
  • 墨西哥
  • 南美洲
  • 巴西
  • 阿根廷
  • 其他的
  • 欧洲
  • 德国
  • 法国
  • 英国
  • 西班牙
  • 其他的
  • 中东和非洲
  • 沙乌地阿拉伯
  • 阿拉伯聯合大公国
  • 以色列
  • 其他的
  • 亚太地区
  • 中国
  • 印度
  • 日本
  • 韩国
  • 其他的

目录

第一章执行摘要

第二章 市场概览

  • 市场概览
  • 市场定义
  • 调查范围

第二章 4. 市场区隔

第三章 商业情境

  • 市场驱动因素
  • 市场限制
  • 市场机会
  • 波特五力分析
  • 产业价值链分析
  • 政策与法规
  • 策略建议

第四章 技术展望

5. 按供应商分類的自动化机器学习 (AUTOML) 市场

  • 介绍
  • 开放原始码
  • Start-Ups公司
  • 科技巨头

6. 按发展阶段分類的自动化机器学习 (AUTOML) 市场

  • 介绍
  • 云端基础的
  • 本地部署

7. 按应用分類的汽车机器学习市场

  • 介绍
  • 诈欺侦测
  • AML侦测
  • 定价
  • 行销与销售管理
  • 其他的

8. 按地区分類的自动化机器学习 (AUTOML) 市场

  • 介绍
  • 北美洲
    • 按提供者
    • 透过使用
    • 按国家/地区
      • 美国
      • 加拿大
      • 墨西哥
  • 南美洲
    • 按提供者
    • 透过使用
    • 按国家/地区
      • 巴西
      • 阿根廷
      • 其他的
  • 欧洲
    • 按提供者
    • 透过使用
    • 按国家/地区
      • 英国
      • 德国
      • 法国
      • 西班牙
      • 其他的
  • 中东和非洲
    • 按提供者
    • 透过使用
    • 按国家/地区
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 以色列
      • 其他的
  • 亚太地区
    • 按提供者
    • 透过使用
    • 按国家/地区
      • 日本
      • 中国
      • 印度
      • 韩国
      • 印尼
      • 泰国
      • 其他的

第九章 竞争格局与分析

  • 主要企业和策略分析
  • 市占率分析
  • 合併、收购、协议和合作
  • 竞争对手仪錶板

第十章：公司简介

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

第十一章调查方法

简介目录
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