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2025年全球自動化機器學習（AutoML）市場報告

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個工作天內

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簡介目錄

近年來，自動化機器學習（AutoML）市場發展迅速，預計將從2024年的16.4億美元成長到2025年的23.4億美元，複合年成長率高達43.1%。預測期內的成長主要受以下因素驅動：機器學習的複雜性、資料科學人才短缺、對快速解決方案的需求、人工智慧和運算能力的進步以及成本效益。

預計未來幾年，自動化機器學習 (AutoML) 市場將呈指數級成長，到 2029 年市場規模將達到 108.8 億美元，複合年成長率 (CAGR) 為 46.8%。預測期內的成長可歸因於跨產業的 AI 整合、物聯網和巨量資料的發展、邊緣運算、混合雲和本地部署解決方案的興起，以及監管合規要求。預測期內的關鍵趨勢包括自動化特徵工程、聯邦學習的進步、可解釋 AI 和模型可解釋性、用於非結構化資料的 AutoML 以及用於自主系統的 AutoML。

自動化機器學習 (AutoML) 是將機器學習應用於實際問題，並實現機器學習模型選擇、配置和參數化的自動化。 AutoML 簡化了機器學習流程，使其更易於使用，與手動編寫的演算法相比，通常能產生更快、更準確的結果。

自動化機器學習 (AutoML) 提供兩大主要服務：解決方案和服務。解決方案是指部署軟體工具來解決特定的組織問題。自動化機器學習解決方案使業務用戶更容易採用機器學習，使資料科學家能夠專注於更複雜的挑戰。這些解決方案可以部署在各種環境中，包括雲端和本地部署，並適用於各種規模的企業。 AutoML 的應用領域包括資料處理、特徵工程、模型選擇、超參數最佳化和調優以及模型組裝。 AutoML 的使用者涵蓋眾多行業，包括銀行、金融服務和保險 (BFSI)、零售和電子商務、醫​​療保健以及製造業。

美國2025年關稅上調及其引發的貿易緊張局勢正對資訊科技產業產生重大影響，尤其是在硬體製造、資料基礎設施和軟體部署方面。進口半導體、電路基板和網路設備的關稅提高，並推高了高科技公司、雲端服務供應商和資料中心的生產和營運成本。在全球範圍內採購筆記型電腦、伺服器和消費電子產品零件的公司面臨更長的前置作業時間週期和價格壓力。同時，對專用軟體徵收的關稅以及主要國際市場的報復性措施擾亂了全球IT供應鏈，並降低了海外對美國製造技術的需求。為了應對這些挑戰，該行業正在加大對國內晶片生產的投資，擴大供應商網路，並利用人工智慧驅動的自動化技術來增強韌性並更有效地控制成本。

這份自動機器學習 (AutoML) 市場研究報告是商業研究公司最新報告系列的一部分，提供市場統計數據，例如全球市場規模、區域佔有率、自動機器學習 (AutoML) 市場佔有率的競爭對手、詳細的市場細分、市場趨勢和商業機會。本市場研究報告對該行業的現狀和未來發展趨勢進行了深入分析，為您提供全面全面的資訊。

我們預測未來五年將成長 46.8%，較先前的預測略微下調 0.2%。這一下調主要歸因於美國與其他國家之間的關稅影響。由於 GPU 和 TPU 等專用處理器的取得管道受限，美國AutoML 市場可能會受到負面影響，因為這些處理器大多產自台灣，並面臨新的貿易障礙。此外，由於相互關稅措施以及貿易緊張局勢和限制升級對全球經濟和貿易造成的負面影響，這種影響也將更加廣泛。

未來幾年，對先進詐欺偵測解決方案日益成長的需求預計將推動自動機器學習 (AutoML) 市場的成長。詐欺偵測是指識別和預防系統或組織內部詐欺活動和行為的過程。自動機器學習 (AutoML) 能夠處理和分析大量數據，識別模式，並發現可能表明詐欺活動的異常情況，從而輔助詐欺偵測。例如，總部位於德國的保險和資產管理公司安聯保險有限公司 (Allianz Insurance plc) 於 2024 年 2 月報告稱，其在 2023 年檢測到 9,520 萬美元（7,740 萬英鎊）的保險詐騙，高於 2022 年的 8,696 萬美元（7,070 萬英鎊）。因此，對先進詐欺偵測解決方案日益成長的需求正在推動自動機器學習 (AutoML) 市場的成長。

物聯網設備的激增預計將推動自動機器學習 (AutoML) 市場的成長。物聯網設備整合了感測器、軟體和其他技術，並透過網際網路與其他設備和系統交換資料。物聯網設備的指數級成長產生了海量數據，這些數據可用於挖掘有價值的洞察。 AutoML 有助於開發機器學習模型，從而從物聯網設備產生的資料中提取有意義的資訊。根據捷克線上媒體公司 TechJury Official 報告，到 2022 年，全球將安裝約 426.2 億個物聯網設備、感測器和致動器，較 2021 年的 358.2 億個和 2020 年的 307.3 億個顯著成長。因此，物聯網設備的成長正在推動自動機器學習 (AutoML) 市場的發展。

自動機器學習 (AutoML) 市場正經歷顯著的技術創新趨勢，領導企業紛紛採用新興先進技術以鞏固其市場地位。例如，總部位於新加坡的金融科技公司 AND Solutions Pte Ltd. 於 2023 年 4 月推出了 NIKO AutoML 平台。 NIKO AutoML 平台是一款尖端的機器學習工具，旨在簡化和加速預測模型的建立。 NIKO AutoML 提供豐富的工具和功能，使用戶無需任何編碼或資料科學專業知識即可快速建立和部署高品質的機器學習模型。其使用者友善的介面引導使用者完成每個步驟，在遠低於傳統方法所需的時間內即可獲得最佳結果。 NIKO AutoML 的主要優勢包括：快速且準確地建立模型、簡化工作流程、提高生產力以及降低成本。

AutoML市場的關鍵參與者致力於開發創新解決方案，例如針對Arm編譯器的AutoML平台。面向Arm編譯器的AutoML平台將AutoML功能整合到Arm編譯器中，從而產生適用於Arm處理器的機器碼。 2023年3月，總部位於東京的電子解決方案製造商TDK株式會社發布了專為輕量級Cortex-M0至M4系列處理器量身打造的「Qeexo AutoML」平台。該平台支援多種機器學習演算法，並在超低延遲和低功耗方面表現出色。 Qeexo AutoML能夠利用感測器資料快速建立和部署機器學習解決方案，使其成為資源受限環境（例如工業、物聯網、穿戴式裝置、汽車和行動裝置）的理想選擇。

2023年5月，德國半導體製造商英飛凌科技股份公司（Infineon Technologies AG）收購了瑞典公司Imagimob AB，具體金額未揭露。此次收購鞏固了英飛凌科技股份公司在蓬勃發展的嵌入式人工智慧解決方案和超緊湊型機器學習市場中的地位，並提升了其在物聯網應用中提供先進功能和節能控制的能力。 Imagimob AB是一家總部位於瑞典的公司，專注於邊緣人工智慧和微型機器學習，致力於為未來的智慧產品提供動力。

自動化機器學習 (AutoML) 市場的主要參與者包括 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，北美是自動機器學習（AutoML）市場最大的地區。預計亞太地區將在預測期內成為成長最快的地區。自動機器學習（AutoML）市場報告涵蓋以下地區：亞太地區、西歐、東歐、北美、南美以及中東和非洲。

自動機器學習 (AutoML) 市場報告涵蓋的國家包括澳洲、巴西、中國、法國、德國、印度、印尼、日本、俄羅斯、韓國、英國、美國、義大利、西班牙和加拿大。

自動化機器學習 (AutoML) 市場包括提供資料視覺化、技術部署、監控和問題解決、詐騙偵測、神經網路架構搜尋(NAS) 以及工作流程最佳化等服務的營業單位所獲得的收入。市場價值包括服務供應商銷售的或包含在其服務產品中的相關商品的價值。僅包括在營業單位之間交易或銷售給最終消費者的商品和服務。

目錄

第1章執行摘要

第2章 市場特徵

第3章 市場趨勢與策略

第4章 市場：宏觀經濟情景，包括利率、通貨膨脹、地緣政治、貿易戰和關稅，以及新冠疫情及其復甦對市場的影響

第5章 全球成長分析與策略分析框架

  • 全球自動化機器學習（AutoML）：PESTEL 分析（政治、社會、技術、環境、法律因素、促進因素和限制因素）
  • 終端用戶產業分析
  • 全球自動機器學習 (AutoML) 市場：成長率分析
  • 全球自動化機器學習 (AutoML) 市場表現：規模與成長，2019-2024 年
  • 全球自動化機器學習 (AutoML) 市場預測：規模與成長，2024-2029 年，2034 年
  • 全球自動化機器學習 (AutoML)：潛在市場規模 (TAM)

第6章 市場細分

  • 全球自動化機器學習 (AutoML) 市場：按產品、效能和預測分類，2019-2024 年、2024-2029 年、2034 年
  • 解決方案
  • 服務
  • 全球自動化機器學習 (AutoML) 市場：按部署、效能和預測分類，2019-2024 年、2024-2029 年、2034 年
  • 雲
  • 本地部署
  • 全球自動化機器學習 (AutoML) 市場：按公司規模、績效和預測分類，2019-2024 年、2024-2029 年、2034 年
  • 小型企業
  • 主要企業
  • 全球自動機器學習 (AutoML) 市場按應用、效能和預測分類，2019-2024 年、2024-2029 年、2034 年
  • 資料處理
  • 特徵工程
  • 模型選擇
  • 超參數最佳化和調優
  • 組裝模型
  • 其他
  • 全球自動化機器學習 (AutoML) 市場按最終用戶、效能和預測分類，2019-2024 年、2024-2029 年、2034 年
  • 銀行、金融服務和保險（BFSI）
  • 零售與電子商務
  • 衛生保健
  • 製造業
  • 其他最終用戶
  • 全球自動機器學習 (AutoML) 市場：解決方案細分、類型、效能和預測（2019-2024 年、2024-2029 年、2034 年）
  • 雲端基礎的解決方案
  • 本地部署解決方案
  • 整合開發環境（IDE）
  • 全球自動機器學習 (AutoML) 市場：服務細分、類型、效能和預測（2019-2024 年、2024-2029 年、2034 年）
  • 諮詢服務
  • 實施服務
  • 培訓和支援服務

第7章 區域和國家分析

  • 全球自動化機器學習 (AutoML) 市場：區域表現及預測，2019-2024 年、2024-2029 年及 2034 年
  • 全球自動機器學習 (AutoML) 市場按國家/地區、效能和預測分類，2019-2024 年、2024-2029 年、2034 年

第8章 亞太市場

第9章：中國市場

第10章 印度市場

第11章 日本市場

第12章：澳洲市場

第13章 印尼市場

第14章 韓國市場

第15章：西歐市場

第16章英國市場

第17章：德國市場

第18章：法國市場

第19章：義大利市場

第20章：西班牙市場

第21章 東歐市場

第22章：俄羅斯市場

第23章 北美市場

第24章美國市場

第25章：加拿大市場

第26章 南美洲市場

第27章：巴西市場

第28章 中東市場

第29章：非洲市場

第30章：競爭格局與公司概況

  • 自動化機器學習（AutoML）市場：競爭格局
  • 自動化機器學習（AutoML）市場：公司概況
    • 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：其他領先和創新企業

  • 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 全球市場競爭基準化分析與儀錶板

第33章 重大併購

第34章 近期市場趨勢

第35章：高潛力市場國家、細分市場與策略

  • 2029年自動化機器學習（AutoML）市場：提供新機會的國家
  • 2029年自動化機器學習（AutoML）市場：提供新機會的細分領域
  • 2029年自動化機器學習（AutoML）市場：成長策略
    • 基於市場趨勢的策略
    • 競爭對手策略

第36章附錄

簡介目錄
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.

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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