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自动机器学习 (AutoML) 市场 - 全球规模、占有率、趋势分析、机会、预测报告，2019-2029

Automated Machine Learning Market - Global Size, Share, Trend Analysis, Opportunity and Forecast Report, 2019-2029, Segmented By Solution ; By Automation Type ; By End User ; By Region
出版日期: | 出版商: Blueweave Consulting | 英文 400 Pages | 商品交期: 2-3个工作天内

价格
简介目录

自动化机器学习（AutoML）的全球市场规模以 44.56% 的复合年增长率快速成长，到 2029 年将达到 87.6 亿美元

由于对高效诈骗侦测解决方案和增强的 ML 专业知识的需求迅速增长，全球自动化机器学习 (AutoML) 市场正在蓬勃发展。

领先的策略咨询和市场研究公司 BlueWeave Consulting 在最近的一项研究中估计，2022 年全球自动化机器学习 (AutoML) 市场规模将达到 9.6 亿美元。 BlueWeave 预测，在 2023-2029 年预测期内，全球自动化机器学习 (AutoML) 市场规模将以 44.56% 的复合年增长率稳步成长，到 2029 年达到 87.6 亿美元。全球自动化机器学习 (AutoML) 市场的主要成长动力包括对先进诈骗侦测解决方案的需求不断增长，这推动了全球 AutoML 市场的成长。资料分析技术，特别是监督神经网络，因其透过预测、丛集和分类等技术诈骗侦测的有效性而受到高度重视。组织预计将投资 AutoML，以增加客户信任并确保合规性。值得注意的是，AutoML 的采用正在加速，因为它可以减少实施和训练 ML 模型所需的知识工作者数量。此外，对 AutoML 的强劲需求主要是因为它能够帮助企业提高洞察力和提高模型准确性，同时最大限度地减少错误和偏差的可能性。 BFSI、医疗保健、IT 和电信以及零售等关键产业预计将为 AutoML 分配资源并加速人工智慧的采用。这包括建立强大的管道来自动化资料事前处理、模型选择和利用预训练模型。特别是在医疗保健领域，人们对用于非接触式筛检的机器学习聊天机器人越来越感兴趣，从而改善整体患者体验。因此，这些方面预计将在预测期内推动全球自动化机器学习（AutoML）市场的扩张。然而，对 AutoML 的认识有限预计将限制分析期间的整体市场成长。

COVID-19 对全球自动化机器学习 (AutoML) 市场的影响

COVID-19 大流行对全球自动化机器学习 (AutoML) 市场产生了各种影响。一方面，这场危机促进了积极的发展，例如加速数位转型配合措施并推动各行业对人工智慧和机器学习解决方案的需求增加。为了应对这场流行病，公司寻求自动化其预测和决策流程，从而导致 AutoML 系统的使用增加。相反，疫情造成了负面影响，扰乱了供应链，迫使企业实施成本削减措施。因此，IT 预算被削减，包括 AutoML 在内的新创新技术的采用也放缓。此外，疫情凸显了对道德和透明人工智慧解决方案的需求，并减缓了缺乏可解释性和透明度的 AutoML 平台的采用。认识到道德和开放式人工智慧解决方案的重要性阻碍了某些 AutoML 平台的快速采用。

全球自动化机器学习 (AutoML) 市场 – 按最终用户划分

按最终用户划分，全球自动化机器学习 (AutoML) 市场分为 BFSI、零售/电子商务、医​​疗保健和製造领域。在预测期内，BFSI 细分市场预计将在最终用户的全球自动化机器学习 (AutoML) 市场中占据最高占有率。 BFSI部门越来越多地利用人工智慧和机器学习来提高业务效率并改善客户体验。对资料的日益关注导致 BFSI 对 ML 应用程式的需求增加。 AutoML 利用大量资料、经济高效的处理能力和经济实惠的储存来提供准确、快速的结果。与金融科技服务的合作使企业能够适应最新的要求和法规，从而提高安全性。由机器学习支援的智慧型流程自动化使金融公司能够自动执行重复性任务并提高生产力。 BFSI 产业处于 AutoML 市场的前沿，主要是因为采用了用于诈骗侦测、风险管理和客户服务的 AI 和 ML 解决方案。

全球自动化机器学习 (AutoML) 市场 – 按地区

关于全球自动机器学习（AutoML）市场的详细研究报告涵盖了五个主要地区（北美、欧洲、亚太地区、拉丁美洲和中东非洲）的各个各国市场。亚太地区主导着全球自动化机器学习 (AutoML) 市场。 IT 支出的增加和金融科技的广泛采用推动了亚太地区的发展。亚太国家政府正积极将人工智慧融入各领域，推动区域市场拓展。中国的机器学习应用大幅成长，企业利用该技术进行金融诈骗侦测、产品推荐和工业流程优化。成功的机器学习倡议依赖强大的基础设施和可靠的资料。受全球机器人、语音辨识和视觉辨识领域人工智慧需求的推动，日本人工智慧市场预计将成长。韩国正在大力投资人工智慧和机器学习等先进技术，预计将为亚太地区 AutoML 市场的成长做出贡献。

竞争形势

全球自动化机器学习 (AutoML) 市场的主要企业包括 DataRobot Inc.、Amazon Web Services Inc.、dotData Inc.、IBM Corporation、Dataiku、SAS Institute Inc.、Microsoft Corporation、Google LLC (Alphabet Inc.)、H2O .ai 、Aible Inc.等为了进一步扩大市场占有率，这些公司正在采取各种策略，例如併购、合作、合资、授权协议和新产品发布。

该报告的详细分析提供了有关全球自动机器学习（AutoML）市场的成长潜力、未来趋势和统计数据的资讯。它还涵盖了推动市场总规模预测的因素。该报告致力于提供全球自动化机器学习（AutoML）市场的最新技术趋势和产业见解，以帮助决策者做出明智的策略决策。此外，我们也分析市场成长动力、挑战和竞争。

目录

第一章 研究框架

第 2 章执行摘要

第 3 章：全球自动机器学习 (AutoML) 市场洞察

  • 产业价值链分析
  • DROC分析
    • 生长促进因子
      • 对高效诈骗侦测方案的需求不断增长
      • 对机器学习专业知识的需求不断增长
    • 抑制因素
      • 认识有限
    • 机会
      • 技术进步
    • 任务
      • 资料品质问题
  • 科技进步/最新发展
  • 法律规范
  • 波特五力分析

第四章全球自动机器学习 (AutoML) 市场概述

  • 2019-2029年市场规模及预测
    • 按金额
  • 市场占有率及预测
    • 按解决方案
      • 独立或本地
      • 云
    • 按自动化类型
      • 资讯
      • 特征工程
      • 造型
      • 视觉化
    • 按最终用户
      • BFSI
      • 零售与电子商务
      • 卫生保健
      • 製造业
      • 其他的
    • 按地区
      • 北美洲
      • 欧洲
      • 亚太地区 (APAC)
      • 拉丁美洲 (LATAM)
      • 中东和非洲 (MEA)

第五章北美自动机器学习（AutoML）市场

  • 2019-2029年市场规模及预测
    • 按金额
  • 市场占有率及预测
    • 按解决方案
    • 按自动化类型
    • 按最终用户
    • 按国家/地区
      • 美国
      • 加拿大

第六章欧洲自动机器学习（AutoML）市场

  • 2019-2029年市场规模及预测
    • 按金额
  • 市场占有率及预测
    • 按解决方案
    • 按自动化类型
    • 按最终用户
    • 按国家/地区
      • 德国
      • 英国
      • 义大利
      • 法国
      • 西班牙
      • 比利时
      • 俄罗斯
      • 荷兰
      • 其他的

第七章亚太地区自动机器学习（AutoML）市场

  • 2019-2029年市场规模及预测
    • 按金额
  • 市场占有率及预测
    • 按解决方案
    • 按自动化类型
    • 按最终用户
    • 按国家/地区
      • 中国
      • 印度
      • 日本
      • 韩国
      • 澳洲和纽西兰
      • 印尼
      • 马来西亚
      • 新加坡
      • 越南
      • 其他的

第八章拉丁美洲自动机器学习（AutoML）市场

  • 2019-2029年市场规模及预测
    • 按金额
  • 市场占有率及预测
    • 按解决方案
    • 按自动化类型
    • 按最终用户
    • 按国家/地区
      • 巴西
      • 墨西哥
      • 阿根廷
      • 秘鲁
      • 其他的

第九章中东和非洲自动机器学习（AutoML）市场

  • 2019-2029年市场规模及预测
    • 按金额
  • 市场占有率及预测
    • 按解决方案
    • 按自动化类型
    • 按最终用户
    • 按国家/地区
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 科威特
      • 南非
      • 奈及利亚
      • 阿尔及利亚
      • 其他的

第10章竞争形势

  • 主要企业及其产品列表
  • 2022年全球自动化机器学习（AutoML）公司市场占有率分析
  • 透过管理参数进行竞争基准化分析
  • 主要策略发展（合併、收购、合作伙伴关係等）

第 11 章 COVID-19 对全球自动机器学习 (AutoML) 市场的影响

第十二章 公司简介（公司简介、财务矩阵、竞争形势、关键人力资源、主要竞争、联络地址、策略展望、SWOT分析）

  • DataRobot Inc.
  • Amazon web services Inc.
  • dotData Inc.
  • IBM Corporation
  • Dataiku
  • SAS Institute Inc.
  • Microsoft Corporation
  • Google LLC（Alphabet Inc.）
  • H2O.ai
  • Aible Inc.
  • 其他主要企业

第十三章 主要战略建议

第14章调查方法

简介目录
Product Code: BWC231049

Global Automated Machine Learning (AutoML) Market Size Booming at Robust CAGR of 44.56% to Reach USD 8.76 Billion by 2029

Global Automated Machine Learning (AutoML) Market is flourishing because of the spurring demand for efficient fraud detection solutions and for enhanced ML expertise.

BlueWeave Consulting, a leading strategic consulting and market research firm, in its recent study, estimated the Global Automated Machine Learning (AutoML) Market size at USD 0.96 billion in 2022. During the forecast period between 2023 and 2029, BlueWeave expects Global Automated Machine Learning (AutoML) Market size to expand at a robust CAGR of 44.56% reaching a value of USD 8.76 billion by 2029. Major growth drivers for the Global Automated Machine Learning (AutoML) Market include an increasing demand for advanced fraud detection solutions is driving the growth of the global AutoML market. Data analysis techniques, particularly supervised neural networks, are highly valued for their effectiveness in fraud detection through methods such as forecasting, clustering, and classification. Organizations are anticipated to invest in AutoML to enhance customer trust and ensure compliance with regulations. Notably, the adoption of AutoML is gaining momentum, as it reduces the number of knowledge workers required for implementing and training ML models. Also, the strong demand for AutoML is primarily driven by its capacity to assist enterprises in improving insights and enhancing model accuracy while minimizing the potential for errors or biases. Major sectors, including BFSI, healthcare, IT & telecom, and retail, are expected to allocate resources to AutoML to accelerate their AI adoption. It involves creating a robust pipeline for automating data preprocessing, model selection, and the utilization of pre-trained models. Notably, the healthcare sector has shown increased interest in ML-powered chatbots for contactless screening, thereby enhancing the overall patient experience. As a result, such aspects are expected to boost the expansion of the Global Automated Machine Learning (AutoML) Market during the forecast period. However, limited awareness about AutoML is anticipated to restrain the overall market growth during the period in analysis.

Impact of COVID-19 on Global Automated Machine Learning (AutoML) Market

COVID-19 pandemic had a mixed impact on the Global Automated Machine Learning (AutoML) Market. On one hand, the crisis spurred positive developments, such as hastening digital transformation efforts and fostering a heightened demand for AI and ML solutions across various sectors. Businesses, in response to the pandemic, sought to automate forecasting and decision-making processes, leading to increased utilization of AutoML systems. Conversely, the pandemic brought adverse effects, disrupting supply chains and compelling businesses to implement cost-cutting measures. It resulted in reduced IT budgets and a deceleration in the adoption of emerging innovative technologies, including AutoML. Furthermore, the pandemic underscored the imperative for ethical and transparent AI solutions, causing a slowdown in the adoption of AutoML platforms that lacked interpretability and transparency. The recognition of the importance of moral and open AI solutions became a hindrance to the swift adoption of certain AutoML platforms.

Global Automated Machine Learning (AutoML) Market - By End User

Based on end user, the Global Automated Machine Learning (AutoML) Market is divided into BFSI, Retail & E-Commerce, Healthcare, and Manufacturing segments. The BFSI segment is expected to hold the highest share in the Global Automated Machine Learning (AutoML) Market by end user during the forecast period. The BFSI sector is increasingly leveraging AI and ML to enhance operational efficiency and improve customer experiences. The growing emphasis on data has led to an increased demand for ML applications in BFSI. AutoML utilizes voluminous data, cost-effective processing capacity, and affordable storage to deliver precise and swift results. Collaborating with fintech services enables businesses to adapt to modern requirements and regulations, ensuring enhanced safety and security. Intelligent process automation, powered by ML, enables finance companies to automate repetitive tasks, leading to increased productivity. The BFSI sector is at the forefront of the AutoML market, primarily due to its adoption of AI and ML solutions for fraud detection, risk management, and customer service.

Global Automated Machine Learning (AutoML) Market - By Region

The in-depth research report on the Global Automated Machine Learning (AutoML) Market covers various country-specific markets across five major regions: North America, Europe, Asia Pacific, Latin America, and Middle East and Africa. Asia Pacific region dominates the Global Automated Machine Learning (AutoML) Market. It is fueled by the escalating IT spending and widespread FinTech adoption. Governments in Asia Pacific countries are actively integrating AI across various sectors, fostering the expansion of local markets. In China, a notable surge in ML adoption is observed, with businesses utilizing the technology for financial fraud detection, product recommendations, and industrial process optimization. The success of ML initiatives relies on robust infrastructure and reliable data. The AI market in Japan is expected to thrive, driven by the global demand for AI in robotics, speech recognition, and visual recognition. South Korea's substantial investments in advanced technologies, including AI and ML, are expected to contribute to the growth of Asia Pacific AutoML Market.

Competitive Landscape

Major players operating in the Global Automated Machine Learning (AutoML) Market include DataRobot Inc., Amazon Web Services Inc., dotData Inc., IBM Corporation, Dataiku, SAS Institute Inc., Microsoft Corporation, Google LLC (Alphabet Inc.), H2O.ai, and Aible Inc. To further enhance their market share, these companies employ various strategies, including mergers and acquisitions, partnerships, joint ventures, license agreements, and new product launches.

The in-depth analysis of the report provides information about growth potential, upcoming trends, and statistics of Global Automated Machine Learning (AutoML) Market. It also highlights the factors driving forecasts of total market size. The report promises to provide recent technology trends in Global Automated Machine Learning (AutoML) Market and industry insights to help decision-makers make sound strategic decisions. Furthermore, the report also analyzes the growth drivers, challenges, and competitive dynamics of the market.

Table of Contents

1. Research Framework

  • 1.1. Research Objective
  • 1.2. Product Overview
  • 1.3. Market Segmentation

2. Executive Summary

3. Global Automated Machine Learning (AutoML) Market Insights

  • 3.1. Industry Value Chain Analysis
  • 3.2. DROC Analysis
    • 3.2.1. Growth Drivers
      • 3.2.1.1. Increasing demand for efficient fraud detection solutions
      • 3.2.1.2. Growing demand for machine learning expertise
    • 3.2.2. Restraints
      • 3.2.2.1. Limited awareness
    • 3.2.3. Opportunities
      • 3.2.3.1. Advancement in technology
    • 3.2.4. Challenges
      • 3.2.4.1. Data quality issues
  • 3.3. Technological Advancements/Recent Developments
  • 3.4. Regulatory Framework
  • 3.5. Porter's Five Forces Analysis
    • 3.5.1. Bargaining Power of Suppliers
    • 3.5.2. Bargaining Power of Buyers
    • 3.5.3. Threat of New Entrants
    • 3.5.4. Threat of Substitutes
    • 3.5.5. Intensity of Rivalry

4. Global Automated Machine Learning (AutoML) Market Overview

  • 4.1. Market Size & Forecast, 2019-2029
    • 4.1.1. By Value (USD Billion)
  • 4.2. Market Share & Forecast
    • 4.2.1. By Solution
      • 4.2.1.1. Standalone or On-Premise
      • 4.2.1.2. Cloud
    • 4.2.2. By Automation Type
      • 4.2.2.1. Data Processing
      • 4.2.2.2. Feature Engineering
      • 4.2.2.3. Modeling
      • 4.2.2.4. Visualization
    • 4.2.3. By End User
      • 4.2.3.1. BFSI
      • 4.2.3.2. Retail & E-Commerce
      • 4.2.3.3. Healthcare
      • 4.2.3.4. Manufacturing
      • 4.2.3.5. Others
    • 4.2.4. By Region
      • 4.2.4.1. North America
      • 4.2.4.2. Europe
      • 4.2.4.3. Asia Pacific (APAC)
      • 4.2.4.4. Latin America (LATAM)
      • 4.2.4.5. Middle East and Africa (MEA)

5. North America Automated Machine Learning (AutoML) Market

  • 5.1. Market Size & Forecast, 2019-2029
    • 5.1.1. By Value (USD Billion)
  • 5.2. Market Share & Forecast
    • 5.2.1. By Solution
    • 5.2.2. By Automation Type
    • 5.2.3. By End User
    • 5.2.4. By Country
      • 5.2.4.1. United States
      • 5.2.4.1.1. By Solution
      • 5.2.4.1.2. By Automation Type
      • 5.2.4.1.3. By End User
      • 5.2.4.2. Canada
      • 5.2.4.2.1. By Solution
      • 5.2.4.2.2. By Automation Type
      • 5.2.4.2.3. By End User

6. Europe Automated Machine Learning (AutoML) Market

  • 6.1. Market Size & Forecast, 2019-2029
    • 6.1.1. By Value (USD Billion)
  • 6.2. Market Share & Forecast
    • 6.2.1. By Solution
    • 6.2.2. By Automation Type
    • 6.2.3. By End User
    • 6.2.4. By Country
      • 6.2.4.1. Germany
      • 6.2.4.1.1. By Solution
      • 6.2.4.1.2. By Automation Type
      • 6.2.4.1.3. By End User
      • 6.2.4.2. United Kingdom
      • 6.2.4.2.1. By Solution
      • 6.2.4.2.2. By Automation Type
      • 6.2.4.2.3. By End User
      • 6.2.4.3. Italy
      • 6.2.4.3.1. By Solution
      • 6.2.4.3.2. By Automation Type
      • 6.2.4.3.3. By End User
      • 6.2.4.4. France
      • 6.2.4.4.1. By Solution
      • 6.2.4.4.2. By Automation Type
      • 6.2.4.4.3. By End User
      • 6.2.4.5. Spain
      • 6.2.4.5.1. By Solution
      • 6.2.4.5.2. By Automation Type
      • 6.2.4.5.3. By End User
      • 6.2.4.6. Belgium
      • 6.2.4.6.1. By Solution
      • 6.2.4.6.2. By Automation Type
      • 6.2.4.6.3. By End User
      • 6.2.4.7. Russia
      • 6.2.4.7.1. By Solution
      • 6.2.4.7.2. By Automation Type
      • 6.2.4.7.3. By End User
      • 6.2.4.8. The Netherlands
      • 6.2.4.8.1. By Solution
      • 6.2.4.8.2. By Automation Type
      • 6.2.4.8.3. By End User
      • 6.2.4.9. Rest of Europe
      • 6.2.4.9.1. By Solution
      • 6.2.4.9.2. By Automation Type
      • 6.2.4.9.3. By End User

7. Asia Pacific Automated Machine Learning (AutoML) Market

  • 7.1. Market Size & Forecast, 2019-2029
    • 7.1.1. By Value (USD Billion)
  • 7.2. Market Share & Forecast
    • 7.2.1. By Solution
    • 7.2.2. By Automation Type
    • 7.2.3. By End User
    • 7.2.4. By Country
      • 7.2.4.1. China
      • 7.2.4.1.1. By Solution
      • 7.2.4.1.2. By Automation Type
      • 7.2.4.1.3. By End User
      • 7.2.4.2. India
      • 7.2.4.2.1. By Solution
      • 7.2.4.2.2. By Automation Type
      • 7.2.4.2.3. By End User
      • 7.2.4.3. Japan
      • 7.2.4.3.1. By Solution
      • 7.2.4.3.2. By Automation Type
      • 7.2.4.3.3. By End User
      • 7.2.4.4. South Korea
      • 7.2.4.4.1. By Solution
      • 7.2.4.4.2. By Automation Type
      • 7.2.4.4.3. By End User
      • 7.2.4.5. Australia & New Zealand
      • 7.2.4.5.1. By Solution
      • 7.2.4.5.2. By Automation Type
      • 7.2.4.5.3. By End User
      • 7.2.4.6. Indonesia
      • 7.2.4.6.1. By Solution
      • 7.2.4.6.2. By Automation Type
      • 7.2.4.6.3. By End User
      • 7.2.4.7. Malaysia
      • 7.2.4.7.1. By Solution
      • 7.2.4.7.2. By Automation Type
      • 7.2.4.7.3. By End User
      • 7.2.4.8. Singapore
      • 7.2.4.8.1. By Solution
      • 7.2.4.8.2. By Automation Type
      • 7.2.4.8.3. By End User
      • 7.2.4.9. Vietnam
      • 7.2.4.9.1. By Solution
      • 7.2.4.9.2. By Automation Type
      • 7.2.4.9.3. By End User
      • 7.2.4.10. Rest of APAC
      • 7.2.4.10.1. By Solution
      • 7.2.4.10.2. By Automation Type
      • 7.2.4.10.3. By End User

8. Latin America Automated Machine Learning (AutoML) Market

  • 8.1. Market Size & Forecast, 2019-2029
    • 8.1.1. By Value (USD Billion)
  • 8.2. Market Share & Forecast
    • 8.2.1. By Solution
    • 8.2.2. By Automation Type
    • 8.2.3. By End User
    • 8.2.4. By Country
      • 8.2.4.1. Brazil
      • 8.2.4.1.1. By Solution
      • 8.2.4.1.2. By Automation Type
      • 8.2.4.1.3. By End User
      • 8.2.4.2. Mexico
      • 8.2.4.2.1. By Solution
      • 8.2.4.2.2. By Automation Type
      • 8.2.4.2.3. By End User
      • 8.2.4.3. Argentina
      • 8.2.4.3.1. By Solution
      • 8.2.4.3.2. By Automation Type
      • 8.2.4.3.3. By End User
      • 8.2.4.4. Peru
      • 8.2.4.4.1. By Solution
      • 8.2.4.4.2. By Automation Type
      • 8.2.4.4.3. By End User
      • 8.2.4.5. Rest of LATAM
      • 8.2.4.5.1. By Solution
      • 8.2.4.5.2. By Automation Type
      • 8.2.4.5.3. By End User

9. Middle East & Africa Automated Machine Learning (AutoML) Market

  • 9.1. Market Size & Forecast, 2019-2029
    • 9.1.1. By Value (USD Billion)
  • 9.2. Market Share & Forecast
    • 9.2.1. By Solution
    • 9.2.2. By Automation Type
    • 9.2.3. By End User
    • 9.2.4. By Country
      • 9.2.4.1. Saudi Arabia
      • 9.2.4.1.1. By Solution
      • 9.2.4.1.2. By Automation Type
      • 9.2.4.1.3. By End User
      • 9.2.4.2. UAE
      • 9.2.4.2.1. By Solution
      • 9.2.4.2.2. By Automation Type
      • 9.2.4.2.3. By End User
      • 9.2.4.3. Qatar
      • 9.2.4.3.1. By Solution
      • 9.2.4.3.2. By Automation Type
      • 9.2.4.3.3. By End User
      • 9.2.4.4. Kuwait
      • 9.2.4.4.1. By Solution
      • 9.2.4.4.2. By Automation Type
      • 9.2.4.4.3. By End User
      • 9.2.4.5. South Africa
      • 9.2.4.5.1. By Solution
      • 9.2.4.5.2. By Automation Type
      • 9.2.4.5.3. By End User
      • 9.2.4.6. Nigeria
      • 9.2.4.6.1. By Solution
      • 9.2.4.6.2. By Automation Type
      • 9.2.4.6.3. By End User
      • 9.2.4.7. Algeria
      • 9.2.4.7.1. By Solution
      • 9.2.4.7.2. By Automation Type
      • 9.2.4.7.3. By End User
      • 9.2.4.8. Rest of MEA
      • 9.2.4.8.1. By Solution
      • 9.2.4.8.2. By Automation Type
      • 9.2.4.8.3. By End User

10. Competitive Landscape

  • 10.1. List of Key Players and Their Offerings
  • 10.2. Global Automated Machine Learning (AutoML) Company Market Share Analysis, 2022
  • 10.3. Competitive Benchmarking, By Operating Parameters
  • 10.4. Key Strategic Developments (Mergers, Acquisitions, Partnerships, etc.)

11. Impact of Covid-19 on Global Automated Machine Learning (AutoML) Market

12. Company Profile (Company Overview, Financial Matrix, Competitive Landscape, Key Personnel, Key Competitors, Contact Address, Strategic Outlook, SWOT Analysis)

  • 12.1. DataRobot Inc.
  • 12.2. Amazon web services Inc.
  • 12.3. dotData Inc.
  • 12.4. IBM Corporation
  • 12.5. Dataiku
  • 12.6. SAS Institute Inc.
  • 12.7. Microsoft Corporation
  • 12.8. Google LLC (Alphabet Inc.)
  • 12.9. H2O.ai
  • 12.10. Aible Inc.
  • 12.11. Other Prominent Players

13. Key Strategic Recommendations

14. Research Methodology

  • 14.1. Qualitative Research
    • 14.1.1. Primary & Secondary Research
  • 14.2. Quantitative Research
  • 14.3. Market Breakdown & Data Triangulation
    • 14.3.1. Secondary Research
    • 14.3.2. Primary Research
  • 14.4. Breakdown of Primary Research Respondents, By Region
  • 14.5. Assumptions & Limitations