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市场调查报告书
商品编码
1428456

2024 年自动化机器学习 (AutoML) 全球市场报告

Automated Machine Learning (AutoML) Global Market Report 2024
出版日期: 按订单生产 | 出版商: The Business Research Company | 英文 175 Pages | 商品交期: 2-10个工作天内

价格
简介目录

自动化机器学习（AutoML）的市场规模预计在未来几年将快速成长。预计到 2028 年将以 44.9% 的复合年增长率 (CAGR) 增长至 73.5 亿美元。预测期内的预期成长可归因于人工智慧在各行业的整合、物联网和巨量资料的普及、边缘运算的出现、混合云端和本地解决方案的采用以及不断增加的监管合规要求。预测期内预期的主要趋势包括自动化特征工程的进步、联邦学习的进步、强调可解释的人工智慧和模型可解释性、AutoML 在非结构化资料中的应用，以及在自动化系统中使用 AutoML。

自动化机器学习 (AutoML) 市场的预期成长是由对进阶诈骗侦测解决方案不断增长的需求所推动的。诈骗侦测涉及识别和防止组织或系统内的诈欺活动或行为。 AutoML 透过有效处理和分析大型资料集、识别模式以及侦测显示潜在诈欺的异常情况，有助于诈欺侦测。例如，美国政府机构金融犯罪执法网络（FinCEN）报告称，2021年，银行机构提交了超过35万份可疑活动报告（SAR）来识别涉嫌支票诈骗，这一数字在2021年有所增加。报告称，2021年，银行机构提交了超过35万份可疑活动报告（SAR），成长了23%。与2017年相比有所增加。 2022年SAR数量持续增加，SAR数量超过68万个，几乎是上一年总合的两倍。因此，对先进诈骗侦测解决方案不断增长的需求是自动化机器学习（AutoML）市场的关键驱动力。

物联网设备的激增必将促进自动化机器学习 (AutoML) 市场的成长。物联网 (IoT) 设备结合了感测器、软体和其他技术，透过互联网与其他设备或系统交换资料。物联网设备的快速成长产生了大量资料，可利用这些数据获得有价值的见解。 AutoML 促进机器学习模型的开发，以从物联网设备产生的资料中提取有意义的资讯。据捷克共和国线上媒体公司TechJury官方称，2022年将安装约426.2亿个物联网设备、感测器和致动器，高于2021年的358.2亿个和2​​020年的307.3亿个。这比100万个大幅增加。物联网设备的兴起将推动自动化机器学习（AutoML）市场的成长。

目录

第一章执行摘要

第二章 市场特点

第三章 市场趋势与策略

第四章宏观经济情景

  • 高通膨对市场的影响
  • 乌克兰与俄罗斯战争对市场的影响
  • COVID-19 对市场的影响

第五章世界市场规模与成长

  • 全球自动化机器学习 (AutoML) 市场的驱动因素和限制因素
    • 市场驱动因素
    • 市场限制因素
  • 2018-2023 年全球自动化机器学习 (AutoML) 市场规模、表现与成长
  • 全球自动化机器学习 (AutoML) 市场规模与成长预测，2023-2028、2033

第六章市场区隔

  • 全球自动化机器学习 (AutoML) 市场，按产品细分、实际和预测，2018-2023、2023-2028、2033
  • 解决方案
  • 服务
  • 全球自动化机器学习 (AutoML) 市场，按部署、实际和预测细分，2018-2023、2023-2028、2033
  • 云
  • 本地
  • 全球自动化机器学习 (AutoML) 市场，按公司细分，实际和预测，2018-2023、2023-2028、2033
  • 中小企业
  • 大公司
  • 全球自动化机器学习 (AutoML) 市场，按应用细分、实际和预测，2018-2023、2023-2028、2033
  • 资讯处理
  • 特征工程
  • 选型
  • 超参数优化和调整
  • 模型合奏
  • 其他的
  • 全球自动化机器学习 (AutoML) 市场，按最终用户细分，实际和预测，2018-2023、2023-2028、2033
  • 银行、金融服务和保险 (BFSI)
  • 零售与电子商务
  • 医疗保健
  • 製造业
  • 其他的

第 7 章 区域与国家分析

  • 按地区分類的全球自动化机器学习 (AutoML) 市场、实际和预测，2018-2023、2023-2028、2033
  • 按国家/地区分類的全球自动机器学习 (AutoML) 市场、实际情况和预测，2018-2023、2023-2028、2033

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14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20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 竞争形势及公司概况

  • 自动化机器学习（AutoML）市场的竞争形势
  • 自动机器学习 (AutoML) 市场公司概况
    • Google LLC
    • Microsoft Corporation
    • Amazon Web Services Inc.
    • International Business Machines Corporation
    • Oracle Corporation

第31章 其他大型创新公司

  • 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章竞争基准化分析

第 33 章. 竞争对手仪表板

第三十四章 重大併购

第35章 未来前景与潜力分析

第36章附录

简介目录
Product Code: r14301

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.

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 $1.15 billion in 2023 to $1.67 billion in 2024 at a compound annual growth rate (CAGR) of 44.9%. The historical period's growth can be attributed to factors such as the complexity of machine learning, a shortage of data science talent, the need for rapid solutions, advancements in AI and computing power, and a focus on cost efficiency.

The automated machine learning (AutoML) market size is expected to see exponential growth in the next few years. It will grow to $7.35 billion in 2028 at a compound annual growth rate (CAGR) of 44.9%. The anticipated growth in the forecast period can be ascribed to the integration of AI across various industries, the proliferation of IoT and big data, the emergence of edge computing, the adoption of hybrid cloud and on-premises solutions, and the increasing regulatory compliance requirements. Key trends expected in the forecast period encompass advancements in automated feature engineering, progress in federated learning, the emphasis on explainable AI and model interpretability, the application of AutoML for unstructured data, and the utilization of AutoML for autonomous systems.

The anticipated growth in the automated machine learning (AutoML) market is driven by the escalating demand for advanced fraud detection solutions. Fraud detection involves the identification and prevention of fraudulent activities or behaviors within an organization or system. AutoML contributes to fraud detection by efficiently processing and analyzing large datasets, identifying patterns, and detecting anomalies indicative of potentially fraudulent activities. For example, the Financial Crimes Enforcement Network (FinCEN), a US government agency, reported that banking institutions sent over 350,000 suspicious activity reports (SARs) in 2021 to identify suspected check fraud, representing a 23% increase compared to 2020. This upward trend continued in 2022, with over 680,000 SARs, nearly doubling the previous year's total. Hence, the increasing need for advanced fraud detection solutions is a key driver 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 September 2021, Qlik Tech International AB, a US-based software company specializing in data analytics and business intelligence solutions, acquired Big Squid Inc. for an undisclosed amount. This acquisition aims to leverage advanced augmented analytics capabilities, enhancing the industry's most robust augmented analytics suite for cloud analytics. Big Squid Inc. is a US-based software company providing no-code automated machine learning (AutoML).

Major companies operating in the automated machine learning (automl) market report are 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 2023. 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 2024 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

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  • Assess the Russia - Ukraine war's impact on agriculture, energy and mineral commodity supply and its direct and indirect impact on the market.
  • Measure the impact of high global inflation on market growth.
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  • Outperform competitors using forecast data and the drivers and trends shaping the market.
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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? 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 impact of sanctions, supply chain disruptions, and altered demand for goods and services due to the Russian Ukraine war, impacting various macro-economic factors and parameters in the Eastern European region and its subsequent effect on global markets.

The impact of higher inflation in many countries and the resulting spike in interest rates.

The continued but declining impact of covid 19 on supply chains and consumption patterns.

  • 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. It covers the growth trajectory of COVID-19 for all regions, key developed countries and major emerging markets.
  • 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
  • Companies Mentioned: Google LLC; Microsoft Corporation; Amazon Web Services Inc.; International Business Machines Corporation; Oracle Corporation
  • 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

  • 4.1. Impact Of High Inflation On The Market
  • 4.2. Ukraine-Russia War Impact On The Market
  • 4.3. COVID-19 Impact On The Market

5. Global Automated Machine Learning (AutoML) Market Size and Growth

  • 5.1. Global Automated Machine Learning (AutoML) Market Drivers and Restraints
    • 5.1.1. Drivers Of The Market
    • 5.1.2. Restraints Of The Market
  • 5.2. Global Automated Machine Learning (AutoML) Historic Market Size and Growth, 2018 - 2023, Value ($ Billion)
  • 5.3. Global Automated Machine Learning (AutoML) Forecast Market Size and Growth, 2023 - 2028, 2033F, Value ($ Billion)

6. Automated Machine Learning (AutoML) Market Segmentation

  • 6.1. Global Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • Solutions
  • Services
  • 6.2. Global Automated Machine Learning (AutoML) Market, Segmentation By Deployment, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • Cloud
  • On-Premises
  • 6.3. Global Automated Machine Learning (AutoML) Market, Segmentation By Enterprise, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • Small And Medium Enterprise
  • Large Enterprise
  • 6.4. Global Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • Data Processing
  • Feature Engineering
  • Model Selection
  • Hyperparameter Optimization And Tuning
  • Model Ensembling
  • Other Applications
  • 6.5. Global Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • Banking, Financial Services And Insurance (BFSI)
  • Retail And E-Commerce
  • Healthcare
  • Manufacturing
  • Other End Users

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

  • 7.1. Global Automated Machine Learning (AutoML) Market, Split By Region, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 7.2. Global Automated Machine Learning (AutoML) Market, Split By Country, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

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

  • 8.1. Asia-Pacific Automated Machine Learning (AutoML) Market Overview
  • Region Information, Impact Of COVID-19, 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 8.3. Asia-Pacific Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 8.4. Asia-Pacific Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F,$ Billion
  • 9.3. China Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F,$ Billion
  • 9.4. China Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F,$ Billion

10. India Automated Machine Learning (AutoML) Market

  • 10.1. India Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 10.2. India Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 10.3. India Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 11.3. Japan Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 11.4. Japan Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

12. Australia Automated Machine Learning (AutoML) Market

  • 12.1. Australia Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 12.2. Australia Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 12.3. Australia Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

13. Indonesia Automated Machine Learning (AutoML) Market

  • 13.1. Indonesia Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 13.2. Indonesia Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 13.3. Indonesia Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 14.3. South Korea Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 14.4. South Korea Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 15.3. Western Europe Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 15.4. Western Europe Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

16. UK Automated Machine Learning (AutoML) Market

  • 16.1. UK Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 16.2. UK Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 16.3. UK Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

17. Germany Automated Machine Learning (AutoML) Market

  • 17.1. Germany Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 17.2. Germany Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 17.3. Germany Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

18. France Automated Machine Learning (AutoML) Market

  • 18.1. France Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 18.2. France Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 18.3. France Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

19. Italy Automated Machine Learning (AutoML) Market

  • 19.1. Italy Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 19.2. Italy Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 19.3. Italy Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

20. Spain Automated Machine Learning (AutoML) Market

  • 20.1. Spain Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 20.2. Spain Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 20.3. Spain Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 21.3. Eastern Europe Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 21.4. Eastern Europe Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

22. Russia Automated Machine Learning (AutoML) Market

  • 22.1. Russia Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 22.2. Russia Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 22.3. Russia Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 23.3. North America Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 23.4. North America Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 24.3. USA Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 24.4. USA Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 25.3. Canada Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 25.4. Canada Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 26.3. South America Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 26.4. South America Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion

27. Brazil Automated Machine Learning (AutoML) Market

  • 27.1. Brazil Automated Machine Learning (AutoML) Market, Segmentation By Offering, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 27.2. Brazil Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 27.3. Brazil Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 28.3. Middle East Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 28.4. Middle East Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 29.3. Africa Automated Machine Learning (AutoML) Market, Segmentation By Application, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ Billion
  • 29.4. Africa Automated Machine Learning (AutoML) Market, Segmentation By End User, Historic and Forecast, 2018-2023, 2023-2028F, 2033F, $ 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
      • 30.2.1.1. Overview
      • 30.2.1.2. Products and Services
      • 30.2.1.3. Strategy
      • 30.2.1.4. Financial Performance
    • 30.2.2. Microsoft Corporation
      • 30.2.2.1. Overview
      • 30.2.2.2. Products and Services
      • 30.2.2.3. Strategy
      • 30.2.2.4. Financial Performance
    • 30.2.3. Amazon Web Services Inc.
      • 30.2.3.1. Overview
      • 30.2.3.2. Products and Services
      • 30.2.3.3. Strategy
      • 30.2.3.4. Financial Performance
    • 30.2.4. International Business Machines Corporation
      • 30.2.4.1. Overview
      • 30.2.4.2. Products and Services
      • 30.2.4.3. Strategy
      • 30.2.4.4. Financial Performance
    • 30.2.5. Oracle Corporation
      • 30.2.5.1. Overview
      • 30.2.5.2. Products and Services
      • 30.2.5.3. Strategy
      • 30.2.5.4. Financial Performance

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

33. Global Automated Machine Learning (AutoML) Market Competitive Dashboard

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

35. Automated Machine Learning (AutoML) Market Future Outlook and Potential Analysis

  • 35.1 Automated Machine Learning (AutoML) Market In 2028 - Countries Offering Most New Opportunities
  • 35.2 Automated Machine Learning (AutoML) Market In 2028 - Segments Offering Most New Opportunities
  • 35.3 Automated Machine Learning (AutoML) Market In 2028 - 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