查看购物车
  • Traditional Chinese
  • English
  • Japanese
封面
市场调查报告书
商品编码
1843667

2025年全球自动化机器学习（AutoML）市场报告

Automated Machine Learning (AutoML) Global Market Report 2025
出版日期: | 出版商: The Business Research Company | 英文 250 Pages | 商品交期: 2-10个工作天内

价格
简介目录

近年来，自动化机器学习（AutoML）市场发展迅速，预计将从2024年的16.4亿美元成长到2025年的23.4亿美元，复合年增长率高达43.1%。预测期内的成长主要受以下因素驱动：机器学习的复杂性、资料科学人才短缺、对快速解决方案的需求、人工智慧和运算能力的进步以及成本效益。

预计未来几年，自动化机器学习 (AutoML) 市场将呈指数级增长，到 2029 年市场规模将达到 108.8 亿美元，复合年增长率 (CAGR) 为 46.8%。预测期内的成长可归因于跨产业的 AI 整合、物联网和巨量资料的发展、边缘运算、混合云和本地部署解决方案的兴起，以及监管合规要求。预测期内的关键趋势包括自动化特征工程、联邦学习的进步、可解释 AI 和模型可解释性、用于非结构化资料的 AutoML 以及用于自主系统的 AutoML。

自动化机器学习 (AutoML) 是将机器学习应用于实际问题，并实现机器学习模型选择、配置和参数化的自动化。 AutoML 简化了机器学习流程，使其更易于使用，与手动编写的演算法相比，通常能产生更快、更准确的结果。

自动化机器学习 (AutoML) 提供两大主要服务：解决方案和服务。解决方案是指部署软体工具来解决特定的组织问题。自动化机器学习解决方案使业务用户更容易采用机器学习，使资料科学家能够专注于更复杂的挑战。这些解决方案可以部署在各种环境中，包括云端和本地部署，并适用于各种规模的企业。 AutoML 的应用领域包括资料处理、特征工程、模型选择、超参数最佳化和调优以及模型组装。 AutoML 的使用者涵盖众多行业，包括银行、金融服务和保险 (BFSI)、零售和电子商务、医​​疗保健以及製造业。

美国2025年关税上调及其引发的贸易紧张局势正对资讯科技产业产生重大影响，尤其是在硬体製造、资料基础设施和软体部署方面。进口半导体、电路基板和网路设备的关税提高，并推高了高科技公司、云端服务供应商和资料中心的生产和营运成本。在全球范围内采购笔记型电脑、伺服器和消费电子产品零件的公司面临更长的前置作业时间週期和价格压力。同时，对专用软体征收的关税以及主要国际市场的报復性措施扰乱了全球IT供应链，并降低了海外对美国製造技术的需求。为了应对这些挑战，该行业正在加大对国内晶片生产的投资，扩大供应商网络，并利用人工智慧驱动的自动化技术来增强韧性并更有效地控製成本。

这份自动机器学习 (AutoML) 市场研究报告是商业研究公司最新报告系列的一部分，提供市场统计数据，例如全球市场规模、区域份额、自动机器学习 (AutoML) 市场份额的竞争对手、详细的市场细分、市场趋势和商业机会。本市场研究报告对该行业的现状和未来发展趋势进行了深入分析，为您提供全面全面的资讯。

我们预测未来五年将成长 46.8%，较先前的预测略微下调 0.2%。这一下调主要归因于美国与其他国家之间的关税影响。由于 GPU 和 TPU 等专用处理器的取得管道受限，美国AutoML 市场可能会受到负面影响，因为这些处理器大多产自台湾，并面临新的贸易障碍。此外，由于相互关税措施以及贸易紧张局势和限制升级对全球经济和贸易造成的负面影响，这种影响也将更加广泛。

未来几年，对先进诈欺侦测解决方案日益增长的需求预计将推动自动机器学习 (AutoML) 市场的成长。诈欺侦测是指识别和预防系统或组织内部诈欺活动和行为的过程。自动机器学习 (AutoML) 能够处理和分析大量数据，识别模式，并发现可能表明诈欺活动的异常情况，从而辅助诈欺侦测。例如，总部位于德国的保险和资产管理公司安联保险有限公司 (Allianz Insurance plc) 于 2024 年 2 月报告称，其在 2023 年检测到 9,520 万美元（7,740 万英镑）的保险诈骗，高于 2022 年的 8,696 万美元（7,070 万英镑）。因此，对先进诈欺侦测解决方案日益增长的需求正在推动自动机器学习 (AutoML) 市场的成长。

物联网设备的激增预计将推动自动机器学习 (AutoML) 市场的成长。物联网设备整合了感测器、软体和其他技术，并透过互联网与其他设备和系统交换资料。物联网设备的指数级增长产生了海量数据，这些数据可用于挖掘有价值的洞察。 AutoML 有助于开发机器学习模型，从而从物联网设备产生的资料中提取有意义的资讯。根据捷克线上媒体公司 TechJury Official 报告，到 2022 年，全球将安装约 426.2 亿个物联网设备、感测器和致动器，较 2021 年的 358.2 亿个和 2020 年的 307.3 亿个显着成长。因此，物联网设备的成长正在推动自动机器学习 (AutoML) 市场的发展。

自动机器学习 (AutoML) 市场正经历显着的技术创新趋势，领导企业纷纷采用新兴先进技术以巩固其市场地位。例如，总部位于新加坡的金融科技公司 AND Solutions Pte Ltd. 于 2023 年 4 月推出了 NIKO AutoML 平台。 NIKO AutoML 平台是一款尖端的机器学习工具，旨在简化和加速预测模型的建立。 NIKO AutoML 提供丰富的工具和功能，使用户无需任何编码或资料科学专业知识即可快速建立和部署高品质的机器学习模型。其使用者友善的介面引导使用者完成每个步骤，在远低于传统方法所需的时间内即可获得最佳结果。 NIKO AutoML 的主要优势包括：快速且准确地建立模型、简化工作流程、提高生产力以及降低成本。

AutoML市场的关键参与者致力于开发创新解决方案，例如针对Arm编译器的AutoML平台。面向Arm编译器的AutoML平台将AutoML功能整合到Arm编译器中，从而产生适用于Arm处理器的机器码。 2023年3月，总部位于东京的电子解决方案製造商TDK株式会社发布了专为轻量级Cortex-M0至M4系列处理器量身打造的「Qeexo AutoML」平台。该平台支援多种机器学习演算法，并在超低延迟和低功耗方面表现出色。 Qeexo AutoML能够利用感测器资料快速建立和部署机器学习解决方案，使其成为资源受限环境（例如工业、物联网、穿戴式装置、汽车和行动装置）的理想选择。

2023年5月，德国半导体製造商英飞凌科技股份公司（Infineon Technologies AG）收购了瑞典公司Imagimob AB，具体金额未揭露。此次收购巩固了英飞凌科技股份公司在蓬勃发展的嵌入式人工智慧解决方案和超紧凑型机器学习市场中的地位，并提升了其在物联网应用中提供先进功能和节能控制的能力。 Imagimob AB是一家总部位于瑞典的公司，专注于边缘人工智慧和微型机器学习，致力于为未来的智慧产品提供动力。

自动化机器学习 (AutoML) 市场的主要参与者包括 Google LLC、Microsoft Corporation、Amazon Web Services Inc.、International Business Machines Corporation、Oracle Corporation、Salesforce Inc.、Teradata Corporation、Alteryx、Altair Engineering Inc.、EdgeVerve Systems Limited、TIBCOcCS Inc.、DataAot Inc.、RapidMiner、Square AI Inc.、Auger.AI、DotData Inc.、BigML Inc.、Valohai、DarwinAI、Aible Inc.、SigOpt、Zerion、Xpanse AI 和 Neptune Labs。

2024年，北美是自动机器学习（AutoML）市场最大的地区。预计亚太地区将在预测期内成为成长最快的地区。自动机器学习（AutoML）市场报告涵盖以下地区：亚太地区、西欧、东欧、北美、南美以及中东和非洲。

自动机器学习 (AutoML) 市场报告涵盖的国家包括澳洲、巴西、中国、法国、德国、印度、印尼、日本、俄罗斯、韩国、英国、美国、义大利、西班牙和加拿大。

自动化机器学习 (AutoML) 市场包括提供资料视觉化、技术部署、监控和问题解决、诈骗侦测、神经网路架构搜寻(NAS) 以及工作流程最佳化等服务的营业单位所获得的收入。市场价值包括服务供应商销售的或包含在其服务产品中的相关商品的价值。仅包括在营业单位之间交易或销售给最终消费者的商品和服务。

目录

第一章执行摘要

第二章 市场特征

第三章 市场趋势与策略

第四章 市场：宏观经济情景，包括利率、通货膨胀、地缘政治、贸易战和关税，以及新冠疫情及其復苏对市场的影响

第五章 全球成长分析与策略分析框架

  • 全球自动化机器学习（AutoML）：PESTEL 分析（政治、社会、技术、环境、法律因素、驱动因素和限制因素）
  • 终端用户产业分析
  • 全球自动机器学习 (AutoML) 市场：成长率分析
  • 全球自动化机器学习 (AutoML) 市场表现：规模与成长，2019-2024 年
  • 全球自动化机器学习 (AutoML) 市场预测：规模与成长，2024-2029 年，2034 年
  • 全球自动化机器学习 (AutoML)：潜在市场规模 (TAM)

第六章 市场细分

  • 全球自动化机器学习 (AutoML) 市场：按产品、效能和预测划分，2019-2024 年、2024-2029 年、2034 年
  • 解决方案
  • 服务
  • 全球自动化机器学习 (AutoML) 市场：按部署、效能和预测划分，2019-2024 年、2024-2029 年、2034 年
  • 云
  • 本地部署
  • 全球自动化机器学习 (AutoML) 市场：按公司规模、绩效和预测划分，2019-2024 年、2024-2029 年、2034 年
  • 小型企业
  • 大公司
  • 全球自动机器学习 (AutoML) 市场按应用、效能和预测划分，2019-2024 年、2024-2029 年、2034 年
  • 资料处理
  • 特征工程
  • 模型选择
  • 超参数最佳化和调优
  • 组装模型
  • 其他的
  • 全球自动化机器学习 (AutoML) 市场按最终用户、效能和预测划分，2019-2024 年、2024-2029 年、2034 年
  • 银行、金融服务和保险（BFSI）
  • 零售与电子商务
  • 卫生保健
  • 製造业
  • 其他最终用户
  • 全球自动机器学习 (AutoML) 市场：解决方案细分、类型、效能和预测（2019-2024 年、2024-2029 年、2034 年）
  • 云端基础的解决方案
  • 本地部署解决方案
  • 整合开发环境（IDE）
  • 全球自动机器学习 (AutoML) 市场：服务细分、类型、效能和预测（2019-2024 年、2024-2029 年、2034 年）
  • 咨询服务
  • 实施服务
  • 培训和支援服务

第七章 区域和国家分析

  • 全球自动化机器学习 (AutoML) 市场：区域表现及预测，2019-2024 年、2024-2029 年及 2034 年
  • 全球自动机器学习 (AutoML) 市场按国家/地区、效能和预测划分，2019-2024 年、2024-2029 年、2034 年

第八章 亚太市场

第九章：中国市场

第十章 印度市场

第十一章 日本市场

第十二章：澳洲市场

第十三章 印尼市场

第十四章 韩国市场

第十五章：西欧市场

第十六章英国市场

第十七章：德国市场

第十八章：法国市场

第十九章：义大利市场

第二十章：西班牙市场

第21章 东欧市场

第22章：俄罗斯市场

第23章 北美市场

第24章美国市场

第25章：加拿大市场

第26章 南美洲市场

第27章：巴西市场

第28章 中东市场

第29章：非洲市场

第三十章：竞争格局与公司概况

  • 自动化机器学习（AutoML）市场：竞争格局
  • 自动化机器学习（AutoML）市场：公司概况
    • Google LLC Overview, Products and Services, Strategy and Financial Analysis
    • Microsoft Corporation Overview, Products and Services, Strategy and Financial Analysis
    • Amazon Web Services Inc. Overview, Products and Services, Strategy and Financial Analysis
    • International Business Machines Corporation Overview, Products and Services, Strategy and Financial Analysis
    • Oracle Corporation Overview, Products and Services, Strategy and Financial Analysis

第31章：其他领先和创新企业

  • Salesforce Inc.
  • Teradata Corporation
  • Alteryx
  • Altair Engineering Inc.
  • EdgeVerve Systems Limited
  • TIBCO Software Inc.
  • DataRobot Inc.
  • Dataiku
  • BigPanda.
  • H2O.ai Inc.
  • KNIME
  • Cognitivescale
  • Anyscale Inc.
  • RapidMiner
  • Squark AI Inc.

第32章 全球市场竞争基准化分析与仪錶板

第33章 重大併购

第34章 近期市场趋势

第35章：高潜力市场国家、细分市场与策略

  • 2029年自动化机器学习（AutoML）市场：提供新机会的国家
  • 2029年自动化机器学习（AutoML）市场：提供新机会的细分领域
  • 2029年自动化机器学习（AutoML）市场：成长策略
    • 基于市场趋势的策略
    • 竞争对手策略

第36章附录

简介目录
Product Code: r24706u

Automated machine learning (AutoML) is the application of machine learning to practical problems, automating the selection, composition, and parameterization of machine learning models. AutoML streamlines the machine learning process, making it more user-friendly and often yielding faster and more accurate outputs compared to manually coded algorithms.

The primary offerings in automated machine learning (AutoML) include solutions and services. Solutions involve the implementation of software tools to address specific organizational issues. Automated machine learning solutions enable business users to easily adopt machine learning, allowing data scientists to focus on more complex challenges. These solutions can be deployed in various settings, such as cloud and on-premises, catering to both small and medium enterprises as well as large enterprises. They find applications in data processing, feature engineering, model selection, hyperparameter optimization and tuning, model assembling, and other areas. AutoML is utilized by various end-users, including industries such as banking, financial services, and insurance (BFSI), retail and e-commerce, healthcare, manufacturing, among others.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report's Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The sharp rise in U.S. tariffs and the ensuing trade tensions in spring 2025 are having a significant impact on the information technology sector, especially in hardware manufacturing, data infrastructure, and software deployment. Increased duties on imported semiconductors, circuit boards, and networking equipment have driven up production and operating costs for tech companies, cloud service providers, and data centers. Firms that depend on globally sourced components for laptops, servers, and consumer electronics are grappling with extended lead times and mounting pricing pressures. At the same time, tariffs on specialized software and retaliatory actions by key international markets have disrupted global IT supply chains and dampened foreign demand for U.S.-made technologies. In response, the sector is ramping up investments in domestic chip production, broadening its supplier network, and leveraging AI-powered automation to improve resilience and manage costs more effectively.

The automated machine learning (AutoML) market research report is one of a series of new reports from The Business Research Company that provides automated machine learning (AutoML) market statistics, including automated machine learning (AutoML) industry global market size, regional shares, competitors with an automated machine learning (AutoML) market share, detailed automated machine learning (AutoML) market segments, market trends and opportunities, and any further data you may need to thrive in the automated machine learning (AutoML) industry. This automated machine learning (AutoML) market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.

The automated machine learning (automl) market size has grown exponentially in recent years. It will grow from $1.64 billion in 2024 to $2.34 billion in 2025 at a compound annual growth rate (CAGR) of 43.1%. The growth in the historic period can be attributed to complexity of machine learning, scarcity of data science talent, demand for speedy solutions, advancements in ai and computing power, cost efficiency.

The automated machine learning (automl) market size is expected to see exponential growth in the next few years. It will grow to $10.88 billion in 2029 at a compound annual growth rate (CAGR) of 46.8%. The growth in the forecast period can be attributed to ai integration across industries, expansion of IoT and big data, rise of edge computing, hybrid cloud and on-premises solutions, regulatory compliance requirements. Major trends in the forecast period include automated feature engineering, federated learning advancements, explainable ai and model interpretability, AutoML for unstructured data, AutoML for autonomous systems.

The forecast of 46.8% growth over the next five years reflects a slight reduction of 0.2% from the previous projection. This reduction is primarily due to the impact of tariffs between the US and other countries. The U.S. AutoML landscape may be negatively impacted by restricted access to specialized processors like GPUs and TPUs, many of which are produced in Taiwan and affected by new trade barriers. The effect will also be felt more widely due to reciprocal tariffs and the negative effect on the global economy and trade due to increased trade tensions and restrictions.

The increasing demand for advanced fraud detection solutions is anticipated to drive the growth of the automated machine learning (AutoML) market in the future. Fraud detection refers to the process of identifying and preventing fraudulent activities or behaviors within a system or organization. Automated machine learning (AutoML) can assist in fraud detection by utilizing its ability to process and analyze large amounts of data, recognize patterns, and identify anomalies that may suggest fraudulent activities. For example, in February 2024, Allianz Insurance plc, a Germany-based company providing insurance and asset management services, reported that $95.2 million (£77.4 million) in claims fraud was detected in 2023, an increase from $86.96 million (£70.7 million) in 2022. Thus, the rising demand for advanced fraud detection solutions is propelling the growth of the automated machine learning (AutoML) market.

The proliferation of IoT devices is poised to contribute to the growth of the automated machine learning (AutoML) market. Internet of Things (IoT) devices, embedded with sensors, software, and other technologies, exchange data with other devices or systems over the internet. The exponential growth in IoT devices results in a vast amount of data that can be utilized for valuable insights. AutoML facilitates the development of machine learning models to extract meaningful information from the data generated by IoT devices. According to TechJury Official, a Czech Republic-based online media company, there were approximately 42.62 billion installed IoT devices, sensors, and actuators in 2022, marking a significant increase from 35.82 billion in 2021 and 30.73 billion in 2020. Consequently, the growing number of IoT devices is a catalyst for the growth of the automated machine learning (AutoML) market.

The automated machine learning (AutoML) market is witnessing a significant trend in technological innovations, with major companies adopting new advancements to maintain their market positions. For example, in April 2023, AND Solutions Pte Ltd., a fintech company based in Singapore, launched the NIKO AutoML platform-a cutting-edge machine-learning tool designed to simplify and accelerate the creation of prediction models. Offering various tools and functionalities, NIKO AutoML enables users to swiftly create and deploy high-quality machine learning models without the need for coding or data science expertise. The user-friendly interface guides users through each stage of the process, delivering optimal results in a fraction of the time required by traditional methods. NIKO AutoML offers key benefits, including fast and accurate model creation, streamlined workflows, increased productivity, and cost-effectiveness.

Major players in the AutoML market are dedicated to developing innovative solutions, such as an AutoML platform for Arm compilers. AutoML for Arm compiler involves integrating AutoML capabilities with the Arm compiler, which generates machine code for Arm processors. In March 2023, TDK Corporation, a Tokyo-based electronic solutions manufacturer, introduced the 'Qeexo AutoML' platform tailored for lightweight Cortex-M0 to -M4 class processors. This platform supports various machine learning algorithms, excelling in ultra-low latency and power consumption. Qeexo AutoML empowers users to rapidly create and implement machine learning solutions using sensor data, making it ideal for deployment in resource-constrained environments such as industrial, IoT, wearables, automotive, and mobile.

In May 2023, Infineon Technologies AG, a Germany-based semiconductor manufacturer, acquired Imagimob AB for an undisclosed sum. This acquisition enables Infineon Technologies to bolster its position in the expanding market for embedded AI solutions and tiny machine learning, improving its ability to provide advanced functionalities and energy-efficient control in IoT applications. Imagimob AB is a Sweden-based company focused on edge AI and tinyML, aimed at facilitating the intelligent products of the future.

Major companies operating in the automated machine learning (AutoML) market include Google LLC, Microsoft Corporation, Amazon Web Services Inc., International Business Machines Corporation, Oracle Corporation, Salesforce Inc., Teradata Corporation, Alteryx, Altair Engineering Inc., EdgeVerve Systems Limited, TIBCO Software Inc., DataRobot Inc., Dataiku, BigPanda., H2O.ai Inc., KNIME, Cognitivescale, Anyscale Inc., RapidMiner, Squark AI Inc., Auger.AI, DotData Inc., BigML Inc., Valohai, DarwinAI, Aible Inc., SigOpt, Zerion, Xpanse AI, Neptune Labs

North America was the largest region in the automated machine learning (AutoML) market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the automated machine learning (automl) market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa

The countries covered in the automated machine learning (automl) market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Italy, Spain, Canada.

The automated machine learning (AutoML) market includes revenues earned by entities by providing data visualization, deployment of technology, monitoring and problem cracking, fraud detection, neural architecture search (NAS), and workflow optimization. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

Automated Machine Learning (AutoML) Global Market Report 2025 from The Business Research Company provides strategists, marketers and senior management with the critical information they need to assess the market.

This report focuses on automated machine learning (automl) market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.

Reasons to Purchase

  • Gain a truly global perspective with the most comprehensive report available on this market covering 15 geographies.
  • Assess the impact of key macro factors such as geopolitical conflicts, trade policies and tariffs, post-pandemic supply chain realignment, inflation and interest rate fluctuations, and evolving regulatory landscapes.
  • Create regional and country strategies on the basis of local data and analysis.
  • Identify growth segments for investment.
  • Outperform competitors using forecast data and the drivers and trends shaping the market.
  • Understand customers based on the latest market shares.
  • Benchmark performance against key competitors.
  • Suitable for supporting your internal and external presentations with reliable high quality data and analysis
  • Report will be updated with the latest data and delivered to you within 2-3 working days of order along with an Excel data sheet for easy data extraction and analysis.
  • All data from the report will also be delivered in an excel dashboard format.

Where is the largest and fastest growing market for automated machine learning (automl) ? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The automated machine learning (automl) market global report from the Business Research Company answers all these questions and many more.

The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market's historic and forecast market growth by geography.

  • The market characteristics section of the report defines and explains the market.
  • The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
  • The forecasts are made after considering the major factors currently impacting the market. These include:

The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.

  • Market segmentations break down the market into sub markets.
  • The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth.
  • The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
  • The trends and strategies section analyses the shape of the market as it emerges from the crisis and suggests how companies can grow as the market recovers.

Scope

  • Markets Covered:1) By Offering: Solutions; Services
  • 2) By Deployment: Cloud; On-Premises
  • 3) By Enterprise: Small And Medium Enterprise; Large Enterprise
  • 4) By Application: Data Processing; Feature Engineering; Model Selection; Hyperparameter Optimization And Tuning; Model Assembling; Other Applications
  • 5) By End User: Banking, Financial Services And Insurance (BFSI); Retail And E-Commerce; Healthcare; Manufacturing; Other End Users
  • Subsegments:
  • 1) By Solutions: Cloud-Based Solutions; On-Premises Solutions; Integrated Development Environments (IDEs)
  • 2) By Services: Consulting Services; Implementation Services; Training And Support Services
  • Companies Mentioned: Google LLC; Microsoft Corporation; Amazon Web Services Inc.; International Business Machines Corporation; Oracle Corporation; Salesforce Inc.; Teradata Corporation; Alteryx; Altair Engineering Inc.; EdgeVerve Systems Limited; TIBCO Software Inc.; DataRobot Inc.; Dataiku; BigPanda.; H2O.ai Inc.; KNIME; Cognitivescale; Anyscale Inc.; RapidMiner; Squark AI Inc.; Auger.AI; DotData Inc.; BigML Inc.; Valohai; DarwinAI; Aible Inc.; SigOpt; Zerion; Xpanse AI; Neptune Labs
  • Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Russia; South Korea; UK; USA; Canada; Italy; Spain
  • Regions: Asia-Pacific; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
  • Time Series: Five years historic and ten years forecast.
  • Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita,
  • Data Segmentations: country and regional historic and forecast data, market share of competitors, market segments.
  • Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
  • Delivery Format: PDF, Word and Excel Data Dashboard.

Table of Contents

1. Executive Summary

2. Automated Machine Learning (AutoML) Market Characteristics

3. Automated Machine Learning (AutoML) Market Trends And Strategies

4. Automated Machine Learning (AutoML) Market - Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics, Trade Wars and Tariffs, And Covid And Recovery On The Market

  • 4.1. Supply Chain Impact from Tariff War & Trade Protectionism

5. Global Automated Machine Learning (AutoML) Growth Analysis And Strategic Analysis Framework

  • 5.1. Global Automated Machine Learning (AutoML) PESTEL Analysis (Political, Social, Technological, Environmental and Legal Factors, Drivers and Restraints)
  • 5.2. Analysis Of End Use Industries
  • 5.3. Global Automated Machine Learning (AutoML) Market Growth Rate Analysis
  • 5.4. Global Automated Machine Learning (AutoML) Historic Market Size and Growth, 2019 - 2024, Value ($ Billion)
  • 5.5. Global Automated Machine Learning (AutoML) Forecast Market Size and Growth, 2024 - 2029, 2034F, Value ($ Billion)
  • 5.6. Global Automated Machine Learning (AutoML) Total Addressable Market (TAM)

6. Automated Machine Learning (AutoML) Market Segmentation

  • 6.1. Global Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Solutions
  • Services
  • 6.2. Global Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Cloud
  • On-Premises
  • 6.3. Global Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Small And Medium Enterprise
  • Large Enterprise
  • 6.4. Global Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Data Processing
  • Feature Engineering
  • Model Selection
  • Hyperparameter Optimization And Tuning
  • Model Assembling
  • Other Applications
  • 6.5. Global Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Banking, Financial Services And Insurance (BFSI)
  • Retail And E-Commerce
  • Healthcare
  • Manufacturing
  • Other End Users
  • 6.6. Global Automated Machine Learning (AutoML) Market, Sub-Segmentation Of Solutions, By Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Cloud-Based Solutions
  • On-Premises Solutions
  • Integrated Development Environments (IDEs)
  • 6.7. Global Automated Machine Learning (AutoML) Market, Sub-Segmentation Of Services, By Type, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • Consulting Services
  • Implementation Services
  • Training And Support Services

7. Automated Machine Learning (AutoML) Market Regional And Country Analysis

  • 7.1. Global Automated Machine Learning (AutoML) Market, Split By Region, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 7.2. Global Automated Machine Learning (AutoML) Market, Split By Country, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

8. Asia-Pacific Automated Machine Learning (AutoML) Market

  • 8.1. Asia-Pacific Automated Machine Learning (AutoML) Market Overview
  • Region Information, Market Information, Background Information, Government Initiatives, Regulations, Regulatory Bodies, Major Associations, Taxes Levied, Corporate Tax Structure, Investments, Major Companies
  • 8.2. Asia-Pacific Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 8.3. Asia-Pacific Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 8.4. Asia-Pacific Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

9. China Automated Machine Learning (AutoML) Market

  • 9.1. China Automated Machine Learning (AutoML) Market Overview
  • 9.2. China Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F,$ Billion
  • 9.3. China Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F,$ Billion
  • 9.4. China Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F,$ Billion

10. India Automated Machine Learning (AutoML) Market

  • 10.1. India Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 10.2. India Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 10.3. India Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

11. Japan Automated Machine Learning (AutoML) Market

  • 11.1. Japan Automated Machine Learning (AutoML) Market Overview
  • 11.2. Japan Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 11.3. Japan Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 11.4. Japan Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

12. Australia Automated Machine Learning (AutoML) Market

  • 12.1. Australia Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 12.2. Australia Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 12.3. Australia Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

13. Indonesia Automated Machine Learning (AutoML) Market

  • 13.1. Indonesia Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 13.2. Indonesia Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 13.3. Indonesia Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

14. South Korea Automated Machine Learning (AutoML) Market

  • 14.1. South Korea Automated Machine Learning (AutoML) Market Overview
  • 14.2. South Korea Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 14.3. South Korea Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 14.4. South Korea Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

15. Western Europe Automated Machine Learning (AutoML) Market

  • 15.1. Western Europe Automated Machine Learning (AutoML) Market Overview
  • 15.2. Western Europe Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 15.3. Western Europe Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 15.4. Western Europe Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

16. UK Automated Machine Learning (AutoML) Market

  • 16.1. UK Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 16.2. UK Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 16.3. UK Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

17. Germany Automated Machine Learning (AutoML) Market

  • 17.1. Germany Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 17.2. Germany Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 17.3. Germany Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

18. France Automated Machine Learning (AutoML) Market

  • 18.1. France Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 18.2. France Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 18.3. France Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

19. Italy Automated Machine Learning (AutoML) Market

  • 19.1. Italy Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 19.2. Italy Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 19.3. Italy Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

20. Spain Automated Machine Learning (AutoML) Market

  • 20.1. Spain Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 20.2. Spain Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 20.3. Spain Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

21. Eastern Europe Automated Machine Learning (AutoML) Market

  • 21.1. Eastern Europe Automated Machine Learning (AutoML) Market Overview
  • 21.2. Eastern Europe Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 21.3. Eastern Europe Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 21.4. Eastern Europe Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

22. Russia Automated Machine Learning (AutoML) Market

  • 22.1. Russia Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 22.2. Russia Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 22.3. Russia Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

23. North America Automated Machine Learning (AutoML) Market

  • 23.1. North America Automated Machine Learning (AutoML) Market Overview
  • 23.2. North America Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 23.3. North America Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 23.4. North America Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

24. USA Automated Machine Learning (AutoML) Market

  • 24.1. USA Automated Machine Learning (AutoML) Market Overview
  • 24.2. USA Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 24.3. USA Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 24.4. USA Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

25. Canada Automated Machine Learning (AutoML) Market

  • 25.1. Canada Automated Machine Learning (AutoML) Market Overview
  • 25.2. Canada Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 25.3. Canada Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 25.4. Canada Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

26. South America Automated Machine Learning (AutoML) Market

  • 26.1. South America Automated Machine Learning (AutoML) Market Overview
  • 26.2. South America Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 26.3. South America Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 26.4. South America Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

27. Brazil Automated Machine Learning (AutoML) Market

  • 27.1. Brazil Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 27.2. Brazil Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 27.3. Brazil Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

28. Middle East Automated Machine Learning (AutoML) Market

  • 28.1. Middle East Automated Machine Learning (AutoML) Market Overview
  • 28.2. Middle East Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 28.3. Middle East Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 28.4. Middle East Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

29. Africa Automated Machine Learning (AutoML) Market

  • 29.1. Africa Automated Machine Learning (AutoML) Market Overview
  • 29.2. Africa Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 29.3. Africa Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion
  • 29.4. Africa Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2019-2024, 2024-2029F, 2034F, $ Billion

30. Automated Machine Learning (AutoML) Market Competitive Landscape And Company Profiles

  • 30.1. Automated Machine Learning (AutoML) Market Competitive Landscape
  • 30.2. Automated Machine Learning (AutoML) Market Company Profiles
    • 30.2.1. Google LLC Overview, Products and Services, Strategy and Financial Analysis
    • 30.2.2. Microsoft Corporation Overview, Products and Services, Strategy and Financial Analysis
    • 30.2.3. Amazon Web Services Inc. Overview, Products and Services, Strategy and Financial Analysis
    • 30.2.4. International Business Machines Corporation Overview, Products and Services, Strategy and Financial Analysis
    • 30.2.5. Oracle Corporation Overview, Products and Services, Strategy and Financial Analysis

31. Automated Machine Learning (AutoML) Market Other Major And Innovative Companies

  • 31.1. Salesforce Inc.
  • 31.2. Teradata Corporation
  • 31.3. Alteryx
  • 31.4. Altair Engineering Inc.
  • 31.5. EdgeVerve Systems Limited
  • 31.6. TIBCO Software Inc.
  • 31.7. DataRobot Inc.
  • 31.8. Dataiku
  • 31.9. BigPanda.
  • 31.10. H2O.ai Inc.
  • 31.11. KNIME
  • 31.12. Cognitivescale
  • 31.13. Anyscale Inc.
  • 31.14. RapidMiner
  • 31.15. Squark AI Inc.

32. Global Automated Machine Learning (AutoML) Market Competitive Benchmarking And Dashboard

33. Key Mergers And Acquisitions In The Automated Machine Learning (AutoML) Market

34. Recent Developments In The Automated Machine Learning (AutoML) Market

35. Automated Machine Learning (AutoML) Market High Potential Countries, Segments and Strategies

  • 35.1 Automated Machine Learning (AutoML) Market In 2029 - Countries Offering Most New Opportunities
  • 35.2 Automated Machine Learning (AutoML) Market In 2029 - Segments Offering Most New Opportunities
  • 35.3 Automated Machine Learning (AutoML) Market In 2029 - Growth Strategies
    • 35.3.1 Market Trend Based Strategies
    • 35.3.2 Competitor Strategies

36. Appendix

  • 36.1. Abbreviations
  • 36.2. Currencies
  • 36.3. Historic And Forecast Inflation Rates
  • 36.4. Research Inquiries
  • 36.5. The Business Research Company
  • 36.6. Copyright And Disclaimer