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自動機器學習 (AutoML) 市場 - 全球規模、佔有率、趨勢分析、機會、預測報告，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個工作天內

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簡介目錄

自動化機器學習（AutoML）的全球市場規模以 44.56% 的複合年成長率快速成長，到 2029 年將達到 87.6 億美元

由於對高效詐騙偵測解決方案和增強的 ML 專業知識的需求迅速成長，全球自動化機器學習 (AutoML) 市場正在蓬勃發展。

領先的策略諮詢和市場研究公司 BlueWeave Consulting 在最近的一項研究中估計，2022 年全球自動化機器學習 (AutoML) 市場規模將達到 9.6 億美元。 BlueWeave 預測，在 2023-2029 年預測期內，全球自動化機器學習 (AutoML) 市場規模將以 44.56% 的複合年成長率穩步成長，到 2029 年達到 87.6 億美元。全球自動化機器學習 (AutoML) 市場的主要成長動力包括對先進詐騙偵測解決方案的需求不斷成長，這推動了全球 AutoML 市場的成長。資料分析技術，特別是監督神經網路，因其透過預測、叢集和分類等技術詐騙偵測的有效性而受到高度重視。組織預計將投資 AutoML，以增加客戶信任並確保合規性。值得注意的是，AutoML 的採用正在加速，因為它可以減少實施和訓練 ML 模型所需的知識工作者數量。此外，對 AutoML 的強勁需求主要是因為它能夠幫助企業提高洞察力和提高模型準確性，同時最大限度地減少錯誤和偏差的可能性。 BFSI、醫療保健、IT 和電信以及零售等關鍵產業預計將為 AutoML 分配資源並加速人工智慧的採用。這包括建立強大的管道來自動化資料事前處理、模型選擇和利用預訓練模型。特別是在醫療保健領域，人們對用於非接觸式篩檢的機器學習聊天機器人越來越感興趣，從而改善整體患者體驗。因此，這些方面預計將在預測期內推動全球自動化機器學習（AutoML）市場的擴張。然而，對 AutoML 的認知有限預計將限制分析期間的整體市場成長。

COVID-19 對全球自動化機器學習 (AutoML) 市場的影響

COVID-19 大流行對全球自動化機器學習 (AutoML) 市場產生了各種影響。一方面，這場危機促進了積極的發展，例如加速數位轉型配合措施並推動各行業對人工智慧和機器學習解決方案的需求增加。為了應對這場流行病，公司尋求自動化其預測和決策流程，從而導致 AutoML 系統的使用增加。相反，疫情造成了負面影響，擾亂了供應鏈，迫使企業實施成本削減措施。因此，IT 預算被削減，包括 AutoML 在內的新創新技術的採用也放緩。此外，疫情凸顯了對道德和透明人工智慧解決方案的需求，並減緩了缺乏可解釋性和透明度的 AutoML 平台的採用。認知到道德和開放式人工智慧解決方案的重要性阻礙了某些 AutoML 平台的快速採用。

全球自動化機器學習 (AutoML) 市場 – 按最終用戶分類

按最終用戶分類，全球自動化機器學習 (AutoML) 市場分為 BFSI、零售/電子商務、醫​​療保健和製造領域。在預測期內，BFSI 細分市場預計將在最終用戶的全球自動化機器學習 (AutoML) 市場中佔據最高佔有率。 BFSI部門擴大利用人工智慧和機器學習來提高業務效率並改善客戶體驗。對資料的日益關注導致 BFSI 對 ML 應用程式的需求增加。 AutoML 利用大量資料、經濟高效的處理能力和經濟實惠的儲存來提供準確、快速的結果。與金融科技服務的合作使企業能夠適應最新的要求和法規，從而提高安全性。由機器學習支援的智慧型流程自動化使金融公司能夠自動執行重複性任務並提高生產力。 BFSI 產業處於 AutoML 市場的前沿，主要是因為採用了用於詐騙偵測、風險管理和客戶服務的 AI 和 ML 解決方案。

全球自動化機器學習 (AutoML) 市場 – 按地區

關於全球自動機器學習（AutoML）市場的詳細研究報告涵蓋了五個主要地區（北美、歐洲、亞太地區、拉丁美洲和中東非洲）的各個各國市場。亞太地區主導著全球自動化機器學習 (AutoML) 市場。 IT 支出的增加和金融科技的廣泛採用推動了亞太地區的發展。亞太國家政府正積極將人工智慧融入各領域，推動區域市場拓展。中國的機器學習應用大幅成長，企業利用該技術進行金融詐騙偵測、產品推薦和工業流程最佳化。成功的機器學習舉措依賴強大的基礎設施和可靠的資料。受全球機器人、語音辨識和視覺辨識領域人工智慧需求的推動，日本人工智慧市場預計將成長。韓國正在大力投資人工智慧和機器學習等先進技術，預計將為亞太地區 AutoML 市場的成長做出貢獻。

競爭形勢

全球自動化機器學習 (AutoML) 市場的主要企業包括 DataRobot Inc.、Amazon Web Services Inc.、dotData Inc.、IBM Corporation、Dataiku、SAS Institute Inc.、Microsoft Corporation、Google LLC (Alphabet Inc.)、H2O .ai 、Aible Inc.等為了進一步擴大市場佔有率，這些公司正在採取各種策略，例如併購、合作、合資、授權協議和新產品發布。

該報告的詳細分析提供了有關全球自動機器學習（AutoML）市場的成長潛力、未來趨勢和統計數據的資訊。它還涵蓋了推動市場總規模預測的因素。該報告致力於提供全球自動化機器學習（AutoML）市場的最新技術趨勢和產業見解，以幫助決策者做出明智的策略決策。此外，我們也分析市場成長動力、挑戰和競爭。

目錄

第1章 研究框架

第 2 章執行摘要

第 3 章：全球自動機器學習 (AutoML) 市場洞察

  • 產業價值鏈分析
  • DROC分析
    • 生長促進因子
      • 對高效詐騙偵測方案的需求不斷成長
      • 對機器學習專業知識的需求不斷成長
    • 抑制因素
      • 認知有限
    • 機會
      • 技術進步
    • 任務
      • 資料品質問題
  • 科技進步/最新發展
  • 法律規範
  • 波特五力分析

第4章全球自動機器學習 (AutoML) 市場概述

  • 2019-2029年市場規模及預測
    • 按金額
  • 市場佔有率及預測
    • 按解決方案
      • 獨立或本地
      • 雲
    • 按自動化類型
      • 資訊
      • 特徵工程
      • 造型
      • 視覺化
    • 按最終用戶
      • BFSI
      • 零售與電子商務
      • 衛生保健
      • 製造業
      • 其他
    • 按地區
      • 北美洲
      • 歐洲
      • 亞太地區 (APAC)
      • 拉丁美洲 (LATAM)
      • 中東和非洲 (MEA)

第5章北美自動機器學習（AutoML）市場

  • 2019-2029年市場規模及預測
    • 按金額
  • 市場佔有率及預測
    • 按解決方案
    • 按自動化類型
    • 按最終用戶
    • 按國家/地區
      • 美國
      • 加拿大

第6章歐洲自動機器學習（AutoML）市場

  • 2019-2029年市場規模及預測
    • 按金額
  • 市場佔有率及預測
    • 按解決方案
    • 按自動化類型
    • 按最終用戶
    • 按國家/地區
      • 德國
      • 英國
      • 義大利
      • 法國
      • 西班牙
      • 比利時
      • 俄羅斯
      • 荷蘭
      • 其他

第7章亞太地區自動機器學習（AutoML）市場

  • 2019-2029年市場規模及預測
    • 按金額
  • 市場佔有率及預測
    • 按解決方案
    • 按自動化類型
    • 按最終用戶
    • 按國家/地區
      • 中國
      • 印度
      • 日本
      • 韓國
      • 澳洲和紐西蘭
      • 印尼
      • 馬來西亞
      • 新加坡
      • 越南
      • 其他

第8章拉丁美洲自動機器學習（AutoML）市場

  • 2019-2029年市場規模及預測
    • 按金額
  • 市場佔有率及預測
    • 按解決方案
    • 按自動化類型
    • 按最終用戶
    • 按國家/地區
      • 巴西
      • 墨西哥
      • 阿根廷
      • 秘魯
      • 其他

第9章中東和非洲自動機器學習（AutoML）市場

  • 2019-2029年市場規模及預測
    • 按金額
  • 市場佔有率及預測
    • 按解決方案
    • 按自動化類型
    • 按最終用戶
    • 按國家/地區
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 科威特
      • 南非
      • 奈及利亞
      • 阿爾及利亞
      • 其他

第10章競爭形勢

  • 主要企業及其產品列表
  • 2022年全球自動化機器學習（AutoML）公司市場佔有率分析
  • 透過管理參數進行競爭基準化分析
  • 主要策略發展（合併、收購、合作夥伴關係等）

第 11 章 COVID-19 對全球自動機器學習 (AutoML) 市場的影響

第12章 公司簡介（公司簡介、財務矩陣、競爭形勢、關鍵人力資源、主要競爭、聯絡地址、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.
  • 其他主要企業

第13章 主要戰略建議

第14章調查方法

簡介目錄
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