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市场调查报告书
商品编码
1517578

自动机器学习 (AutoML) 市场规模 - 按产品、部署模式、企业规模、应用程式、最终用户和预测，2024 年至 2032 年

Automated Machine Learning (AutoML) Market Size - By Offering, By Deployment Mode, By Enterprise Size, By Application, By End-User & Forecast, 2024 - 2032
出版日期: | 出版商: Global Market Insights Inc. | 英文 260 Pages | 商品交期: 2-3个工作天内

价格
简介目录

由于企业领导者之间的策略对话不断增加，2024 年至 2032 年全球自动化机器学习 (AutoML) 市场的复合年增长率将超过 30%。这些合作结合了人工智慧、资料科学和云端运算方面的专业知识，以创新和提供强大的 AutoML 解决方案。透过集成，领先公司正在透过自动化模型建置、特征工程和超参数优化功能来增强其平台。

例如，2023 年 9 月，富士通有限公司与 Linux 基金会合作，在西班牙毕尔巴鄂举行的 2023 年欧洲开源高峰会之前，正式推出了自动化机器学习和人工智慧公平技术作为开源软体 (OSS)。这些名为 SapientML 和 Intersection Fairness 的措施旨在为使用者提供自动产生新机器学习模型程式码并解决训练资料偏差的工具。

这种连接正在加速人工智慧在医疗保健、金融和零售等行业的采用，而强大的资料分析对于这些行业至关重要。这些合作伙伴关係也扩大了市场范围，支援根据客户需求客製化解决方案，从新创公司到企业级组织。随着竞争的加剧，AutoML 市场的联盟支持预测分析和机器学习的创新，从而提高效率和可扩展性。最终，这些合作伙伴关係促进了技术进步，并使人工智慧驱动的见解变得容易获得。

整体自动化机器学习 (AutoML) 行业规模根据产品、部署模式、企业规模、应用程式、最终用户和区域进行分类。

从 2024 年到 2032 年，该服务领域的自动化机器学习 (AutoML) 市场收入将实现令人称讚的复合年增长率。专业人员提供咨询、修改和实施管理，为各行业创建客製化的 AutoML 解决方案。公司正在使用这些服务来加快原型设计、提高准确性并更好地将人工智慧整合到其营运中。随着越来越重视资料驱动的决策，企业越来越依赖服务提供者来应对实施人工智慧的挑战，确保灵活性和合规性。随着对复杂人工智慧功能的需求增加，AutoML 市场服务领域不断扩大。

从 2024 年到 2032 年，本地市场将资料成长。园区内的 AutoML 平台使企业能够更好地控制其资料和工作流程，确保关键资讯保留在其基础设施内。这种部署模式对医疗保健、金融和政府等行业也很有吸引力，这些行业严格的规则很重要。透过采用本地 AutoML 解决方案，企业可以提高营运效率、减少资料处理时间并遵守本地和国际资料保护法规。虽然组织降低了利用人工智慧功能所需的风险，但对基于位置的 AutoML 解决方案的需求仍在不断增长。

亚太地区自动化机器学习 (AutoML) 市场从 2024 年到 2032 年将呈现显着的复合年增长率。製造、医疗保健和零售业使用 AutoML 来简化营运、改善决策流程并获得竞争优势。 AutoML 解决方案着重可扩充性和效率，可满足动态市场环境中的业务需求。政府促进人工智慧创新的政策支持对人工智慧能力的合作和投资，进一步刺激市场成长。随着亚太地区经济体接受人工智慧驱动的洞察，对 AutoML 解决方案的需求不断扩大，塑造新产业的未来。

目录

第 1 章：方法与范围

第 2 章：执行摘要

第 3 章：产业洞察

  • 产业生态系统分析
  • 供应商格局
    • 技术提供者
    • 服务供应商
    • 平台提供者
    • 终端用户
  • 利润率分析
  • 技术与创新格局
  • 专利分析
  • 重要新闻和倡议
  • 监管环境
  • 衝击力
    • 成长动力
      • 对人工智慧解决方案的需求不断增长
      • 缺乏熟练的资料科学家
      • 与云端服务整合的增加
      • 客製化选项和灵活性的提高
    • 产业陷阱与挑战
      • 引起对资料隐私的担忧
      • 资料和模型的复杂性
  • 成长潜力分析
  • 波特的分析
  • PESTEL分析

第 4 章：竞争格局

  • 介绍
  • 公司市占率分析
  • 竞争定位矩阵
  • 战略展望矩阵

第 5 章：市场估计与预测：按 2021 - 2032 年发行

  • 主要趋势
  • 解决方案
  • 服务
    • 咨询
    • 一体化
    • 部署

第 6 章：市场估计与预测：依部署模式，2021 - 2032 年

  • 主要趋势
  • 云
  • 本地

第 7 章：市场估计与预测：依企业规模，2021 - 2032

  • 主要趋势
  • 中小企业
    • 解决方案
    • 服务
      • 咨询
      • 一体化
      • 部署
  • 大型企业
    • 解决方案
    • 服务
      • 咨询
      • 一体化
      • 部署

第 8 章：市场估计与预测：依应用分类，2021 - 2032

  • 主要趋势
  • 资料处理
  • 特征工程
  • 选型
  • 超参数优化和调整
  • 模特儿合奏
  • 其他的

第 9 章：市场估计与预测：按最终用户划分，2021 - 2032 年

  • 主要趋势
  • 资讯科技与电信
  • BFSI
  • 零售
  • 汽车
  • 媒体与娱乐
  • 其他的

第 10 章：市场估计与预测：按地区，2021 - 2032

  • 主要趋势
  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 俄罗斯
    • 西班牙
    • 欧洲其他地区
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 东南亚
    • 亚太地区其他地区
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
    • 拉丁美洲其他地区
  • MEA
    • 阿联酋
    • 南非
    • 沙乌地阿拉伯
    • MEA 的其余部分

第 11 章：公司简介

  • Alphabet Inc.
  • Alteryx
  • Amazon Web Services, Inc.
  • Auger.AI
  • BigML
  • DarwinAI
  • Databricks AutoML
  • Dataiku
  • DataRobot MLOps
  • DataRobot Paxata
  • DataRobot, Inc.
  • DotData
  • Feature Labs
  • H2O.ai
  • HPE Haven OnDemand
  • IBM Corporation
  • KNIME
  • Microsoft
  • RapidMiner Auto Model
  • TIBCO Software Inc.
简介目录
Product Code: 9033

Global Automated Machine Learning (AutoML) Market will observe a CAGR of over 30% from 2024 to 2032 due to rising strategic conversations between business leaders. These collaborations combine expertise in AI, data science, and cloud computing to innovate and deliver robust AutoML solutions. Through integration, leading companies are enhancing their platforms with automated model building, feature engineering, and hyperparameter optimization capabilities.

For instance, in September 2023, Fujitsu Limited, in collaboration with the Linux Foundation, officially launched its automated machine learning and AI fairness technologies as open-source software (OSS) ahead of the Open Source Summit Europe 2023 in Bilbao, Spain. These initiatives, named SapientML and Intersectional Fairness,aim to provide users with tools that automatically generate code for new machine learning models and address biases in training data.

This connectivity is accelerating the adoption of AI in industries such as healthcare, finance, and retail, where robust data analytics are essential. These partnerships also expand market reach, enabling solutions tailored to the needs of customers, from start-ups to enterprise-level organizations. As competition intensifies, alliances in the AutoML market support innovation in predictive analytics and machine learning, improving efficiency and scalability. Ultimately, these partnerships spur technological advancements and make AI-driven insights accessible.

Overall Automated Machine Learning (AutoML) Industry size is classified based on offering, deployment mode, enterprise size, application, end-user, and region.

The Automated Machine Learning (AutoML) market revenue from the service segment will register a commendable CAGR from 2024 to 2032. The services are popular due to the need for basic skills in implementing and managing machine learning models. Professionals provide consulting, modification, and implementation management to create customized AutoML solutions for various industries. Companies are using these services to speed up prototyping, improve accuracy, and better integrate AI into their operations. With an increasing emphasis on data-driven decision-making, companies increasingly rely on service providers to navigate the challenges of implementing AI, ensuring flexibility and compliance. As demand for sophisticated AI capabilities increases, the AutoML market service segment continues to expand.

The on-premises segment will witness an appreciable growth from 2024 to 2032. The demand for on-premises solutions addresses an organization's prioritization of data privacy, security, and compliance. AutoML platforms on campus give enterprises greater control over their data and workflow, ensuring critical information stays within their infrastructure. This deployment model is also attractive to industries such as healthcare, finance, and government, where strict rules are important. By adopting on-premise AutoML solutions, businesses increase operational efficiencies, reduce data processing time, and comply with local and international data protection regulations. While organizations reduce the risk required to leverage AI capabilities, the demand for location-based AutoML solutions continues to grow.

Asia Pacific automated machine learning (AutoML) market will exhibit a notable CAGR from 2024 to 2032. The demand in the region is driven by rapid digital transformation and increasing adoption of AI technologies. Businesses in manufacturing, healthcare, and retail use AutoML to streamline operations, improve decision-making processes, and gain competitive advantage. With a focus on scalability and efficiency, AutoML solutions meet business needs in a dynamic market environment. Governments' policies to promote AI innovation support partnerships and investments in AI capabilities, further stimulating market growth. As Asia Pacific economies embrace AI-powered insights, the demand for AutoML solutions continues to expand, shaping the future of new industries.

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates and calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimates
  • 1.3 Forecast model
  • 1.4 Primary research & validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market definitions

Chapter 2 Executive Summary

  • 2.1 Industry 360 degree synopsis, 2021 - 2032

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
  • 3.2 Supplier landscape
    • 3.2.1 Technology providers
    • 3.2.2 Service providers
    • 3.2.3 Platform providers
    • 3.2.4 End users
  • 3.3 Profit margin analysis
  • 3.4 Technology & innovation landscape
  • 3.5 Patent analysis
  • 3.6 Key news & initiatives
  • 3.7 Regulatory landscape
  • 3.8 Impact forces
    • 3.8.1 Growth drivers
      • 3.8.1.1 Growing demand for ai solutions
      • 3.8.1.2 Shortage of skilled data scientists
      • 3.8.1.3 Rise in the integration with cloud services
      • 3.8.1.4 Rise in the customization options and flexibility
    • 3.8.2 Industry pitfalls & challenges
      • 3.8.2.1 Raising concerns about data privacy
      • 3.8.2.2 Complexity of data and models
  • 3.9 Growth potential analysis
  • 3.10 Porter's analysis
  • 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2023

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Offering 2021 - 2032 ($Mn)

  • 5.1 Key trends
  • 5.2 Solution
  • 5.3 Service
    • 5.3.1 Consulting
    • 5.3.2 Integration
    • 5.3.3 Deployment

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2032 ($Mn)

  • 6.1 Key trends
  • 6.2 Cloud
  • 6.3 On-premises

Chapter 7 Market Estimates & Forecast, By Enterprise size, 2021 - 2032 ($Mn)

  • 7.1 Key trends
  • 7.2 SMEs
    • 7.2.1 Solution
    • 7.2.2 Service
      • 7.2.2.1 Consulting
      • 7.2.2.2 Integration
      • 7.2.2.3 Deployment
  • 7.3 Large enterprises
    • 7.3.1 Solution
    • 7.3.2 Service
      • 7.3.2.1 Consulting
      • 7.3.2.2 Integration
      • 7.3.2.3 Deployment

Chapter 8 Market Estimates & Forecast, By Application, 2021 - 2032 ($Mn)

  • 8.1 Key trends
  • 8.2 Data processing
  • 8.3 Feature engineering
  • 8.4 Model selection
  • 8.5 Hyperparameter optimization & tuning
  • 8.6 Model ensemble
  • 8.7 Others

Chapter 9 Market Estimates & Forecast, By End-User, 2021 - 2032 ($Mn)

  • 9.1 Key trends
  • 9.2 IT & telecommunications
  • 9.3 BFSI
  • 9.4 Retail
  • 9.5 Automotive
  • 9.6 Media & entertainment
  • 9.7 Others

Chapter 10 Market Estimates & Forecast, By Region, 2021 - 2032 ($Mn)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 U.S.
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 UK
    • 10.3.2 Germany
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Russia
    • 10.3.6 Spain
    • 10.3.7 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 Japan
    • 10.4.3 India
    • 10.4.4 South Korea
    • 10.4.5 Australia
    • 10.4.6 Southeast Asia
    • 10.4.7 Rest of Asia Pacific
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
    • 10.5.4 Rest of Latin America
  • 10.6 MEA
    • 10.6.1 UAE
    • 10.6.2 South Africa
    • 10.6.3 Saudi Arabia
    • 10.6.4 Rest of MEA

Chapter 11 Company Profiles

  • 11.1 Alphabet Inc.
  • 11.2 Alteryx
  • 11.3 Amazon Web Services, Inc.
  • 11.4 Auger.AI
  • 11.5 BigML
  • 11.6 DarwinAI
  • 11.7 Databricks AutoML
  • 11.8 Dataiku
  • 11.9 DataRobot MLOps
  • 11.10 DataRobot Paxata
  • 11.11 DataRobot, Inc.
  • 11.12 DotData
  • 11.13 Feature Labs
  • 11.14 H2O.ai
  • 11.15 HPE Haven OnDemand
  • 11.16 IBM Corporation
  • 11.17 KNIME
  • 11.18 Microsoft
  • 11.19 RapidMiner Auto Model
  • 11.20 TIBCO Software Inc.