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機器學習市場－全球產業規模、佔有率、趨勢、機會及預測（按組件、公司規模、部署方式、最終用戶、地區和競爭格局分類，2021-2031年）

Machine Learning, Market - Global Industry Size, Share, Trends, Opportunity, and Forecast. Segmented By Component, By Enterprises Size, By Deployment, By End-User, By Region & Competition, 2021-2031F
出版日期: | 出版商: TechSci Research | 英文 180 Pages | 商品交期: 2-3個工作天內

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簡介目錄

全球機器學習 (ML) 市場預計將從 2025 年的 761.3 億美元大幅成長至 2031 年的 5,793.9 億美元，複合年成長率達 40.25%。

機器學習被定義為人工智慧的一個專門分支，它利用資料而非明確的程式指令來識別模式並提升演算法效能。巨量資料爆炸式成長以及高效能運算透過雲端運算基礎設施的普及，是推動這一市場成長的根本動力，使各行各業的企業能夠實現複雜工作流程的自動化並從中獲得可執行的洞察。

市場概覽
預測期 2027-2031
市場規模：2025年 761.3億美元
市場規模：2031年 5793.9億美元
複合年成長率：2026-2031年 40.25%
成長最快的細分市場 雲
最大的市場 北美洲

市場發展的一大障礙是缺乏具備建構和維護複雜模型架構技能的專業人才。這種人才短缺造成了營運瓶頸，並增加了尋求擴大規模的組織的人事費用。儘管面臨這些挑戰，這項技術仍是經營團隊的首要策略重點。根據電氣及電子工程師學會，到 2024 年，全球 65% 的技術領導者會將人工智慧 (AI) 和機器學習列為當年最重要的技術領域。

市場促進因素

將生成式人工智慧應用於智慧自動化和內容生成，正從根本上重塑全球機器學習 (ML) 市場，使其效用超越了標準的預測任務。隨著企業尋求利用能夠合成文字、程式碼和媒體的模型來簡化營運並提高生產力，這項促進因素正推動資本配置激增。關注點正從實驗性試點轉向可擴展的部署，使演算法能夠自主處理複雜的工作流程。史丹佛大學人性化人工智慧研究所於 2025 年 4 月發布的《2025 年人工智慧指數報告》顯示，到 2024 年，私人對生成式人工智慧的投資將達到 339 億美元，這將推動先進神經網路架構的發展。

同時，基於雲端的機器學習即服務 (MLaaS) 的普及，透過消除高昂的本地硬體成本，正在使這些先進工具的獲取變得更加民主化。雲端平台為各種規模的組織提供了高效訓練和部署模型所需的可擴展基礎設施，使企業能夠將人工智慧功能直接整合到其現有的數位生態系統中，而無需大量的初始投資。例如，SiliconANGLE 在 2025 年 8 月報道稱，微軟 Azure AI 服務每季創造了約 30 億美元的收入。此外，OpenAI 在 2025 年 12 月發布的報告《企業人工智慧現況》指出，75% 的員工在使用人工智慧後，工作速度和品質均有所提升。

市場挑戰

熟練專業人才短缺是限制全球機器學習市場規模擴張的主要障礙。各組織在取得開發和維護複雜模型架構所需的技術專長方面面臨巨大挑戰，導致營運瓶頸。人才短缺造成人事費用上升和計劃即時延長，常常迫使企業延後或縮減自動化策略，直接降低機器學習投資的實際價值，並減緩其更廣泛的商業性應用。

技術能力與勞動力準備之間的差距嚴重限制了市場發展勢頭：世界經濟論壇的數據顯示，94%的商業領袖表示，到2025年，他們將面臨人工智慧關鍵人才短缺的問題。這項數據凸顯了瓶頸的嚴重性：如果沒有合格的監管，現有的計算能力和數據就無法得到有效利用，從而造成了結構性成長瓶頸，由於實施過程中的實際困難，對機器學習解決方案的需求無法得到滿足。

市場趨勢

全球機器學習市場正經歷一場變革，從被動的預測模型向主動系統轉型，這些系統能夠自主規劃和執行多步驟工作流程，而無需人工干預。這項變革使企業能夠部署能夠自主推理複雜業務流程的數位員工，其功能遠超簡單的內容產生。這項技術已成為一項策略重點，並即時推動了資本投入。根據 UiPath 於 2025 年 2 月發布的《2025 年主動式人工智慧調查報告》，45% 的美國 IT 高階主管計劃在當年投資主動式人工智慧，以增強業務自動化。

同時，各組織正積極採用邊緣人工智慧，在設備本地處理數據，以降低延遲並減輕集中式雲端儲存帶來的隱私風險。這種去中心化有助於工業IoT和行動應用實現即時決策，同時確保在斷網環境下的功能正常運作。這種向設備端處理的架構轉變也反映在企業的支出趨勢上。根據ZEDEDA於2025年5月發布的《邊緣人工智慧成熟度報告》，90%的組織計劃在2025年增加其邊緣人工智慧預算，以擴展分散式能力並實現高效、低延遲的運算。

目錄

第1章概述

第2章調查方法

第3章執行摘要

第4章：客戶評價

第5章 全球機器學習（ML）市場展望

  • 市場規模及預測
    • 按金額
  • 市佔率及預測
    • 按組件（服務和解決方案）
    • 依公司規模（中小企業、大型企業）
    • 按部署方式（雲端/本地端）
    • 按最終用戶分類（醫療保健、零售、IT和電信、汽車和運輸、廣告和媒體、銀行、金融服務和保險、政府、國防、其他）
    • 按地區
    • 按公司（2025 年）
  • 市場地圖

第6章：北美機器學習（ML）市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 北美洲：國家分析
    • 美國
    • 加拿大
    • 墨西哥

7. 歐洲機器學習 (ML) 市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙

第8章：亞太地區機器學習（ML）市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

9. 中東與非洲機器學習 (ML) 市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

第10章：南美機器學習（ML）市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷

第11章 市場動態

  • 促進要素
  • 任務

第12章 市場趨勢與發展

  • 併購
  • 產品發布
  • 最新進展

第13章 全球機器學習（ML）市場：SWOT分析

第14章：波特五力分析

  • 產業競爭
  • 新進入者的可能性
  • 供應商電力
  • 顧客權力
  • 替代品的威脅

第15章 競爭格局

  • Amazon Web Services, Inc
  • Baidu, Inc
  • Domino Data Lab, Inc
  • Microsoft Corporation
  • Google, Inc
  • Alpine Data
  • IBM Corporation
  • SAP SE
  • Intel Corporation
  • SAS Institute Inc.

第16章 策略建議

第17章：關於研究公司及免責聲明

簡介目錄
Product Code: 14561

The Global Machine Learning (ML) Market is projected to expand significantly, growing from USD 76.13 Billion in 2025 to USD 579.39 Billion by 2031, reflecting a CAGR of 40.25%. Defined as a specialized subset of artificial intelligence, machine learning utilizes algorithms to identify patterns and refine performance using data rather than explicit programming instructions. This market growth is fundamentally propelled by the exponential availability of big data and the democratization of powerful computing through cloud infrastructure, enabling enterprises across various sectors to automate complex workflows and derive actionable intelligence.

Market Overview
Forecast Period2027-2031
Market Size 2025USD 76.13 Billion
Market Size 2031USD 579.39 Billion
CAGR 2026-203140.25%
Fastest Growing SegmentCloud
Largest MarketNorth America

A major obstacle hindering faster market development is the shortage of skilled professionals qualified to build and maintain complex model architectures. This talent gap creates operational bottlenecks for organizations attempting to scale their initiatives and leads to increased labor costs. Despite these challenges, the technology remains a top strategic priority for executives; according to the Institute of Electrical and Electronics Engineers, 65 percent of global technology leaders in 2024 identified artificial intelligence and machine learning as the most critical technology area for the year.

Market Driver

The integration of generative AI for intelligent automation and content creation is fundamentally reshaping the Global Machine Learning (ML) Market by extending utility beyond standard predictive tasks. This driver has triggered a surge in capital allocation as enterprises aim to utilize models capable of synthesizing text, code, and media to streamline operations and boost productivity. The focus has moved from experimental pilots to scalable deployments where algorithms autonomously handle complex workflows; according to the Stanford Institute for Human-Centered Artificial Intelligence's '2025 AI Index Report' from April 2025, private investment in generative AI hit $33.9 billion in 2024, fueling the development of sophisticated neural architectures.

Concurrently, the widespread adoption of cloud-based Machine Learning as a Service (MLaaS) is democratizing access to these advanced tools by eliminating the prohibitive costs of on-premises hardware. Cloud platforms offer the scalable infrastructure necessary for organizations of all sizes to train and deploy models efficiently, allowing businesses to integrate AI capabilities directly into existing digital ecosystems without heavy upfront capital expenditure. Highlighting this demand, SiliconANGLE reported in August 2025 that Microsoft's Azure AI services generated approximately $3 billion in quarterly revenue, while an OpenAI report titled 'The state of enterprise AI' in December 2025 noted that 75 percent of workers experienced improved output speed or quality using AI.

Market Challenge

The shortage of skilled professionals acts as a primary barrier to the scalable expansion of the Global Machine Learning Market. Organizations face significant difficulties in securing the technical expertise necessary to develop and maintain complex model architectures, resulting in immediate operational bottlenecks. This deficit in talent leads to inflated labor costs and extended project timelines, often forcing enterprises to delay or downsize their automation strategies, which directly reduces the realizable value of machine learning investments and slows broader commercial adoption.

This gap between technological capability and workforce readiness places a substantial restraint on market momentum. According to the World Economic Forum, 94 percent of business leaders in 2025 reported facing shortages in talent critical for artificial intelligence functions. This statistic emphasizes the severity of the bottleneck, as available computing power and data cannot be effectively leveraged without qualified human oversight, creating a structural ceiling on growth where the demand for machine learning solutions remains unfulfilled due to the practical incapacity to implement them.

Market Trends

The Global Machine Learning Market is undergoing a transformative shift from passive predictive models to agentic systems capable of autonomous planning and executing multi-step workflows without human intervention. This evolution enables enterprises to deploy digital workers that reason through complex business processes independently, advancing capabilities significantly beyond simple content generation. This technology has become a strategic priority driving immediate capital allocation; according to UiPath's '2025 Agentic AI Research Report' from February 2025, 45 percent of U.S. IT executives indicated readiness to invest in agentic AI during the year to enhance operational automation.

Simultaneously, organizations are aggressively adopting Edge AI to process data locally on devices, thereby reducing latency and mitigating privacy risks associated with centralized cloud storage. This decentralization facilitates real-time decision-making for industrial IoT and mobile applications while ensuring functionality in disconnected environments. This architectural move toward on-device processing is reflected in corporate spending; according to ZEDEDA's 'Edge AI Matures' report from May 2025, 90 percent of organizations plan to increase their edge AI budgets for 2025 to scale these distributed capabilities and support efficient, low-latency computing.

Key Market Players

  • Amazon Web Services, Inc
  • Baidu, Inc
  • Domino Data Lab, Inc
  • Microsoft Corporation
  • Google, Inc
  • Alpine Data
  • IBM Corporation
  • SAP SE
  • Intel Corporation
  • SAS Institute Inc.

Report Scope

In this report, the Global Machine Learning (ML) Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Machine Learning (ML) Market, By Component

  • Services
  • Solutions

Machine Learning (ML) Market, By Enterprises Size

  • SMEs
  • Large Enterprises

Machine Learning (ML) Market, By Deployment

  • Cloud
  • On-premises

Machine Learning (ML) Market, By End-User

  • Healthcare
  • Retailer
  • IT & Telecom
  • Automotive and Transports
  • Advertising & Media
  • BFSI
  • Government
  • Defense
  • Others

Machine Learning (ML) Market, By Region

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Machine Learning (ML) Market.

Available Customizations:

Global Machine Learning (ML) Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Machine Learning (ML) Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Component (Services & Solutions)
    • 5.2.2. By Enterprises Size (SMEs, Large Enterprises)
    • 5.2.3. By Deployment (Cloud, On-premises)
    • 5.2.4. By End-User (Healthcare, Retailer, IT & Telecom, Automotive and Transports, Advertising & Media, BFSI, Government, Defense, Others)
    • 5.2.5. By Region
    • 5.2.6. By Company (2025)
  • 5.3. Market Map

6. North America Machine Learning (ML) Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Component
    • 6.2.2. By Enterprises Size
    • 6.2.3. By Deployment
    • 6.2.4. By End-User
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Machine Learning (ML) Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Component
        • 6.3.1.2.2. By Enterprises Size
        • 6.3.1.2.3. By Deployment
        • 6.3.1.2.4. By End-User
    • 6.3.2. Canada Machine Learning (ML) Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Component
        • 6.3.2.2.2. By Enterprises Size
        • 6.3.2.2.3. By Deployment
        • 6.3.2.2.4. By End-User
    • 6.3.3. Mexico Machine Learning (ML) Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Component
        • 6.3.3.2.2. By Enterprises Size
        • 6.3.3.2.3. By Deployment
        • 6.3.3.2.4. By End-User

7. Europe Machine Learning (ML) Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Component
    • 7.2.2. By Enterprises Size
    • 7.2.3. By Deployment
    • 7.2.4. By End-User
    • 7.2.5. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Machine Learning (ML) Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Component
        • 7.3.1.2.2. By Enterprises Size
        • 7.3.1.2.3. By Deployment
        • 7.3.1.2.4. By End-User
    • 7.3.2. France Machine Learning (ML) Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Component
        • 7.3.2.2.2. By Enterprises Size
        • 7.3.2.2.3. By Deployment
        • 7.3.2.2.4. By End-User
    • 7.3.3. United Kingdom Machine Learning (ML) Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Component
        • 7.3.3.2.2. By Enterprises Size
        • 7.3.3.2.3. By Deployment
        • 7.3.3.2.4. By End-User
    • 7.3.4. Italy Machine Learning (ML) Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Component
        • 7.3.4.2.2. By Enterprises Size
        • 7.3.4.2.3. By Deployment
        • 7.3.4.2.4. By End-User
    • 7.3.5. Spain Machine Learning (ML) Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Component
        • 7.3.5.2.2. By Enterprises Size
        • 7.3.5.2.3. By Deployment
        • 7.3.5.2.4. By End-User

8. Asia Pacific Machine Learning (ML) Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Component
    • 8.2.2. By Enterprises Size
    • 8.2.3. By Deployment
    • 8.2.4. By End-User
    • 8.2.5. By Country
  • 8.3. Asia Pacific: Country Analysis
    • 8.3.1. China Machine Learning (ML) Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Component
        • 8.3.1.2.2. By Enterprises Size
        • 8.3.1.2.3. By Deployment
        • 8.3.1.2.4. By End-User
    • 8.3.2. India Machine Learning (ML) Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Component
        • 8.3.2.2.2. By Enterprises Size
        • 8.3.2.2.3. By Deployment
        • 8.3.2.2.4. By End-User
    • 8.3.3. Japan Machine Learning (ML) Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Component
        • 8.3.3.2.2. By Enterprises Size
        • 8.3.3.2.3. By Deployment
        • 8.3.3.2.4. By End-User
    • 8.3.4. South Korea Machine Learning (ML) Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Component
        • 8.3.4.2.2. By Enterprises Size
        • 8.3.4.2.3. By Deployment
        • 8.3.4.2.4. By End-User
    • 8.3.5. Australia Machine Learning (ML) Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Component
        • 8.3.5.2.2. By Enterprises Size
        • 8.3.5.2.3. By Deployment
        • 8.3.5.2.4. By End-User

9. Middle East & Africa Machine Learning (ML) Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Component
    • 9.2.2. By Enterprises Size
    • 9.2.3. By Deployment
    • 9.2.4. By End-User
    • 9.2.5. By Country
  • 9.3. Middle East & Africa: Country Analysis
    • 9.3.1. Saudi Arabia Machine Learning (ML) Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Component
        • 9.3.1.2.2. By Enterprises Size
        • 9.3.1.2.3. By Deployment
        • 9.3.1.2.4. By End-User
    • 9.3.2. UAE Machine Learning (ML) Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Component
        • 9.3.2.2.2. By Enterprises Size
        • 9.3.2.2.3. By Deployment
        • 9.3.2.2.4. By End-User
    • 9.3.3. South Africa Machine Learning (ML) Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Component
        • 9.3.3.2.2. By Enterprises Size
        • 9.3.3.2.3. By Deployment
        • 9.3.3.2.4. By End-User

10. South America Machine Learning (ML) Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Component
    • 10.2.2. By Enterprises Size
    • 10.2.3. By Deployment
    • 10.2.4. By End-User
    • 10.2.5. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Machine Learning (ML) Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Component
        • 10.3.1.2.2. By Enterprises Size
        • 10.3.1.2.3. By Deployment
        • 10.3.1.2.4. By End-User
    • 10.3.2. Colombia Machine Learning (ML) Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Component
        • 10.3.2.2.2. By Enterprises Size
        • 10.3.2.2.3. By Deployment
        • 10.3.2.2.4. By End-User
    • 10.3.3. Argentina Machine Learning (ML) Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Component
        • 10.3.3.2.2. By Enterprises Size
        • 10.3.3.2.3. By Deployment
        • 10.3.3.2.4. By End-User

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Merger & Acquisition (If Any)
  • 12.2. Product Launches (If Any)
  • 12.3. Recent Developments

13. Global Machine Learning (ML) Market: SWOT Analysis

14. Porter's Five Forces Analysis

  • 14.1. Competition in the Industry
  • 14.2. Potential of New Entrants
  • 14.3. Power of Suppliers
  • 14.4. Power of Customers
  • 14.5. Threat of Substitute Products

15. Competitive Landscape

  • 15.1. Amazon Web Services, Inc
    • 15.1.1. Business Overview
    • 15.1.2. Products & Services
    • 15.1.3. Recent Developments
    • 15.1.4. Key Personnel
    • 15.1.5. SWOT Analysis
  • 15.2. Baidu, Inc
  • 15.3. Domino Data Lab, Inc
  • 15.4. Microsoft Corporation
  • 15.5. Google, Inc
  • 15.6. Alpine Data
  • 15.7. IBM Corporation
  • 15.8. SAP SE
  • 15.9. Intel Corporation
  • 15.10. SAS Institute Inc.

16. Strategic Recommendations

17. About Us & Disclaimer