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市場調查報告書
商品編碼
2071179

汽車產業雲端數據 DevOps 和 MLOps 平台的市場機會、成長要素、產業趨勢分析和 2026-2035 年預測。

Automotive Cloud Data DevOps and MLOps Platforms Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2026 - 2035
出版日期: | 出版商: Global Market Insights Inc. | 英文 295 Pages | 商品交期: 2-3個工作天內

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簡介目錄

全球汽車雲端數據 DevOps 和 MLOps 平台市場預計到 2025 年將達到 8.124 億美元，年複合成長率為 22.4%，到 2035 年將達到 59 億美元。

汽車雲端資料 DevOps 與 MLOps 平台市場-IMG1

隨著汽車產業從傳統的軟體開發方法轉向旨在管理整個車輛軟體生命週期的整合式雲端原生生態系統，汽車雲端資料DevOps和MLOps平台市場正經歷重大變革。軟體定義車輛技術的日益普及，加速了對能夠支援持續軟體開發、部署、監控和機器學習操作的複雜平台的需求。汽車製造商越來越依賴雲端環境來簡化軟體工程流程、提高開發效率並管理日益複雜的車輛架構。人工智慧、聯網汽車汽車技術和先進軟體功能的整合，推動了對可擴展平台的更大需求，這些平台能夠協調跨多個相關人員者的開發、測試、檢驗、部署和維運工作流程。這些平台在實現即時資料管理、軟體最佳化、基於模擬的測試和持續應用交付方面發揮關鍵作用。產業法規和不斷發展的汽車標準也在推動這些解決方案在全球車輛開發生態系統中得到更廣泛的應用。從區域來看，北美憑藉其強大的雲端基礎設施能力和對以軟體為中心的車輛開發的早期投資，仍然是領先的市場。同時，在監管合規要求和汽車工程流程數位化進步的推動下，歐洲預計將繼續保持顯著成長。

市場範圍
開始年份 2025
預測期 2026-2035
上市時的市場規模 8.124億美元
預測金額 59億美元
複合年成長率 22.4%

隨著汽車製造商優先考慮先進的軟體管理能力以支援日益複雜的汽車技術，汽車雲端數據 DevOps 和 MLOps 平台市場持續成長。軟體開發、檢驗、部署和維護活動之間無縫協調的需求日益成長，推動了對整合平台環境的需求。這些解決方案有助於企業提高營運效率、縮短軟體部署時間，並在複雜的汽車軟體生態系統中保持效能一致性。隨著連網解決方案和智慧汽車技術的不斷發展，預計在預測期內，可擴展的雲端 DevOps 和 MLOps 環境的重要性將顯著提升。

預計到2025年，DevOps平台市佔率將達到50%，並在2026年至2035年間以17%的複合年成長率成長。該平台作為汽車軟體開發的基礎，發揮核心作用，支援持續整合、軟體交付、自動化測試、原始碼管理和軟體部署流程。隨著汽車製造商尋求在確保可靠性、網路安全和整體產品品質的同時加快軟體發布週期，對DevOps平台的需求日益成長。這些平台在管理軟體部署工作流程和支援現代汽車生態系統中高效的軟體生命週期管理方面變得越來越重要。隨著軟體定義車輛架構的日益複雜和軟體更新頻率的增加，整個汽車產業對DevOps解決方案的需求持續成長。

軟體平台細分市場預計在2025年將佔據42.6%的市場佔有率，並在2026年至2035年間以24.6%的複合年成長率成長。軟體平台作為市場的核心編配環境，在一個統一的生態系統中支援應用開發、機器學習操作、測試、模擬、配置和軟體生命週期管理。這些解決方案提供可擴展的雲端原生基礎設施，支援在日益複雜的汽車軟體環境中實現持續軟體交付和維運管理。汽車製造商和技術供應商利用這些平台協調開發活動、管理分散式團隊，並利用車輛產生的數據持續改進軟體。隨著軟體定義汽車（SDV）策略的不斷擴展，軟體平台對於端到端軟體生命週期管理的重要性日益凸顯。

預計2025年，中國汽車雲端數據DevOps和MLOps平台市場規模將達1.176億美元，市佔率高達53%。推動中國市場成長的主要因素包括軟體定義汽車（SDV）的快速發展、電動車技術的日益普及以及人工智慧（AI）在汽車系統中的日益融合。隨著汽車製造商持續投資於聯網汽車技術和雲端原生軟體架構，對先進DevOps和MLOps平台的需求也顯著成長。持續的軟體更新、智慧車輛功能以及可擴展的軟體管理能力的需求，為中國汽車生態系統中的平台提供者創造了巨大的商機。預計這些趨勢將進一步鞏固中國在預測期內引領區域市場成長的領先地位。

目錄

第1章：調查方法

第2章執行摘要

第3章 行業洞察

  • 產業生態系分析
    • 供應商情況
    • 利潤率分析
    • 成本結構
    • 每個階段增加的價值
    • 影響價值鏈的因素
    • 中斷
  • 影響產業的因素
    • 促進因素
      • 軟體定義車輛（SDV）的普及
      • 自動駕駛和高級駕駛輔助系統的發展
      • 聯網汽車數據的爆炸性成長
      • 向雲端原生汽車架構遷移
    • 產業潛在風險與挑戰
      • 資料安全和監管合規挑戰
      • 與傳統汽車系統整合的複雜性
    • 市場機遇
      • 空中下載 (OTA) 軟體貨幣化模式的興起
      • 利用人工智慧擴展預測性維護和車輛智慧
      • 數位雙胞胎和基於模擬的開發技術的發展
      • 原始設備製造商 (OEM) 與超大規模資料中心業者)之間合作關係的加強
  • 成長潛力分析
  • 技術與創新展望
    • 最新科技趨勢
    • 新興技術
  • 價格分析
    • 對過去價格趨勢的分析
    • 定價策略：按業務類型分類
  • 監理情勢
    • 北美洲
      • 美國國家公路交通安全管理局（NHTSA）
      • 美國聯邦通訊委員會（FCC）
      • 美國運輸部（USDOT）
      • 美國聯邦貿易委員會 (FTC) 資料隱私規則
      • ISO/SAE 21434 網路安全標準
    • 歐洲
      • 聯合國歐洲經濟委員會工作小組29（R155和R156）
      • 一般資料保護規則（GDPR）
      • 歐盟資料法
      • 歐盟一般安全法規 (GSR)
      • ISO 26262 功能安全標準
    • 亞太地區
      • 中國的網路安全法
      • 中國的資料安全法
      • 中國個人資料保護法（PIPL）
      • 日本汽車軟體與行動安全框架
      • 印度汽車產業發展計畫（AMP）
    • 拉丁美洲
      • 巴西通用資料保護法（LGPD）
      • 墨西哥汽車產業的數位化與資料管治政策
      • 南方共同市場數位一體化框架
      • 智慧運輸法規
    • 中東和非洲
      • 阿拉伯聯合大公國人工智慧戰略和數據法規
      • 沙烏地阿拉伯資料與人工智慧管理局（SDAIA）規章
      • 海灣合作理事會的數位經濟與智慧運輸框架
      • 非洲聯盟數位轉型策略
      • 非洲自由貿易區（AfCFTA）數位協議
  • 波特的分析
  • PESTLE分析
  • 專利分析
  • 貿易數據分析
    • 進出口量及進口額趨勢
    • 主要貿易路線及關稅的影響
  • 成本細分分析
  • 人工智慧和生成式人工智慧對市場的影響
    • 利用人工智慧改造現有經營模式
    • 按細分市場分類的生成式人工智慧用例和部署藍圖
    • 風險、限制和監管考量
  • 生產能力和生產情況
    • 設備產能：按地區和主要生產商分類
    • 運轉率和擴張計劃
  • 永續性和環境方面
    • 永續計劃
    • 減少廢棄物策略
    • 生產中的能源效率
    • 具有環保意識的舉措
    • 考慮碳足跡
  • 預測假設和情境分析
    • 基本案例：驅動複合年成長率的關鍵宏觀經濟與產業變量
    • 樂觀情境：宏觀經濟與產業的順風
    • 悲觀情景：宏觀經濟放緩或產業逆風

第4章 競爭情勢

  • 介紹
  • 企業市佔率分析
    • 北美洲
    • 歐洲
    • 亞太地區
    • LATAM
    • 中東和非洲
  • 主要市場公司的競爭分析
  • 競爭定位矩陣
  • 主要進展
    • 併購
    • 夥伴關係和聯盟
    • 新產品發布
    • 業務拓展計劃及資金籌措
  • 按公司規模進行基準測試
    • 排名分類標準與選擇標準
    • 按銷售額、地區和創新能力分類的層級定位矩陣。

第5章 市場估計與預測：依平台分類，2022-2035年

  • DevOps平台
  • MLOps平台
  • 整合式 DevOps-MLOps 平台

第6章 市場估計與預測：依解法分類，2022-2035年

  • 軟體平台
  • 基礎架構和資料管理工具
  • 服務
    • 專業服務
    • 託管服務

第7章 市場估算與預測：依部署模式分類，2022-2035年

  • 公共雲端
  • 私有雲端
  • 混合雲端

第8章 市場估計與預測：依公司規模分類，2022-2035年

  • 大公司
  • 中小企業

第9章 市場估計與預測：依應用領域分類，2022-2035年

  • 汽車自動駕駛與安全
  • 聯網汽車服務
  • 車輛和資產管理
  • 預測性維護和可靠性
  • 製造和供應鏈分析
  • 其他

第10章 市場估價與預測：依地區分類，2022-2035年

  • 北美洲
    • 美國
    • 加拿大
  • 歐洲
    • 德國
    • 英國
    • 法國
    • 義大利
    • 西班牙
    • 俄羅斯
    • 挪威
    • 荷蘭
    • 瑞典
  • 亞太地區
    • 中國
    • 印度
    • 日本
    • 澳洲
    • 韓國
    • 新加坡
    • 泰國
    • 印尼
    • 越南
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • 中東和非洲
    • 南非
    • 沙烏地阿拉伯
    • UAE
    • 土耳其

第11章：公司簡介

  • 世界公司
    • Amazon Web Services（AWS）
    • Microsoft Azure
    • Google Cloud
    • IBM
    • Oracle
    • NVIDIA
    • Databricks
    • Snowflake
    • SAP
    • VMware（Broadcom）
    • Palantir Technologies
    • Siemens
    • Cloudera
    • Salesforce
    • ServiceNow
    • Atlassian
  • 當地公司
    • DataRobot
    • H2O.ai
    • SAS Institute
    • Scale AI
簡介目錄
Product Code: 15913

The Global Automotive Cloud Data DevOps and MLOps Platforms Market was valued at USD 812.4 million in 2025 and is estimated to grow at a CAGR of 22.4% to reach USD 5.9 billion by 2035.

Automotive Cloud Data DevOps and MLOps Platforms Market - IMG1

The automotive cloud data DevOps and MLOps platforms market is experiencing a significant transformation as the automotive industry shifts from traditional software development approaches toward integrated cloud-native ecosystems designed to manage the entire vehicle software lifecycle. Growing adoption of software-defined vehicle technologies is accelerating demand for advanced platforms capable of supporting continuous software development, deployment, monitoring, and machine learning operations. Automotive manufacturers are increasingly relying on cloud-based environments to streamline software engineering processes, improve development efficiency, and manage increasingly complex vehicle architectures. The rising integration of artificial intelligence, connected vehicle technologies, and advanced software functionalities is creating the need for scalable platforms that can orchestrate development, testing, validation, deployment, and operational workflows across multiple stakeholders. These platforms play a critical role in enabling real-time data management, software optimization, simulation-based testing, and continuous application delivery. Industry regulations and evolving automotive standards are also encouraging broader adoption of these solutions across global vehicle development ecosystems. Regionally, North America remains a leading market due to its strong cloud infrastructure capabilities and early investments in software-centric vehicle development, while Europe continues to witness substantial growth driven by regulatory compliance requirements and increasing digitalization of automotive engineering processes.

Market Scope
Start Year2025
Forecast Year2026-2035
Start Value$812.4 Million
Forecast Value$5.9 Billion
CAGR22.4%

The automotive cloud data DevOps and MLOps platforms market continues to gain momentum as vehicle manufacturers prioritize advanced software management capabilities to support increasingly sophisticated automotive technologies. The need for seamless coordination between software development, validation, deployment, and maintenance activities is driving demand for unified platform environments. These solutions help organizations improve operational efficiency, reduce software deployment timelines, and maintain consistent performance across complex automotive software ecosystems. As connected mobility solutions and intelligent vehicle technologies continue to evolve, the importance of scalable cloud-based DevOps and MLOps environments is expected to increase significantly throughout the forecast period.

The DevOps platforms segment accounted for 50% share in 2025 and is anticipated to grow at a CAGR of 17% between 2026 and 2035. This segment serves as the core foundation of automotive software development by supporting continuous integration, software delivery, automated testing, source code management, and software deployment processes. Demand for DevOps platforms is increasing as automotive manufacturers seek to accelerate software release cycles while ensuring reliability, cybersecurity, and overall product quality. These platforms are becoming increasingly important in managing software deployment workflows and supporting efficient software lifecycle management within modern vehicle ecosystems. The growing complexity of software-defined vehicle architectures and the increasing frequency of software updates continue to strengthen demand for DevOps solutions across the automotive industry.

The software platforms segment held a 42.6% share in 2025 and is forecast to grow at a CAGR of 24.6% from 2026 to 2035. Software platforms serve as the central orchestration environment within the market, enabling application development, machine learning operations, testing, simulation, deployment, and software lifecycle management within a unified ecosystem. These solutions provide scalable cloud-native infrastructures that support continuous software delivery and operational management across increasingly complex automotive software environments. Automotive manufacturers and technology suppliers are utilizing these platforms to coordinate development activities, manage distributed teams, and leverage vehicle-generated data for ongoing software improvements. As software-defined vehicle strategies continue to expand, software platforms are becoming increasingly essential for end-to-end software lifecycle management.

China Automotive Cloud Data Devops and Mlops Platforms Market held a 53% share, generating USD 117.6 million in 2025. Market growth in the country is being fueled by rapid advancements in software-defined vehicle development, expanding adoption of electric mobility technologies, and increasing integration of artificial intelligence across automotive systems. As automotive manufacturers continue investing in connected vehicle technologies and cloud-native software architectures, demand for advanced DevOps and MLOps platforms is rising significantly. The need to support continuous software updates, intelligent vehicle functionalities, and scalable software management capabilities is creating substantial opportunities for platform providers across the Chinese automotive ecosystem. These trends are expected to strengthen the country's position as a leading contributor to regional market growth over the forecast period.

Major companies operating in the Global Automotive Cloud Data Devops and Mlops Platforms Market include Amazon Web Services, Microsoft, NVIDIA, Databricks, IBM, Oracle, Google, GitLab, Snowflake, and VMware. Companies active in the automotive cloud data DevOps and MLOps platforms market are implementing a variety of strategies to strengthen their market position and expand their competitive advantage. Product innovation remains a primary focus, with vendors developing advanced cloud-native platforms that integrate software development, machine learning operations, data management, and deployment capabilities within unified environments. Strategic collaborations with automotive manufacturers, technology providers, and mobility solution developers are helping companies expand their customer base and accelerate platform adoption. Significant investments in artificial intelligence, automation technologies, cybersecurity capabilities, and scalable cloud infrastructure are enhancing platform functionality and operational performance. Organizations are also focusing on expanding regional presence, strengthening partner ecosystems, and improving interoperability with automotive software environments.

Table of Contents

Chapter 1 Research Methodology

  • 1.1 Research approach
  • 1.2 Quality Commitments
    • 1.2.1 GMI AI policy & data integrity commitment
      • 1.2.1.1 Source consistency protocol
  • 1.3 Research Trail & Confidence Scoring
    • 1.3.1 Research Trail Components
    • 1.3.2 Scoring Components
  • 1.4 Data Collection
    • 1.4.1 Partial list of primary sources
  • 1.5 Data mining sources
    • 1.5.1 Paid sources
      • 1.5.1.1 Sources, by region
  • 1.6 Base estimates and calculations
    • 1.6.1 Base year calculation for any one approach
  • 1.7 Forecast model
    • 1.7.1 Quantified market impact analysis
      • 1.7.1.1 Mathematical impact of growth parameters on forecast
  • 1.8 Research transparency addendum
    • 1.8.1 Source attribution framework
    • 1.8.2 Quality assurance metrics
    • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

  • 2.1 Industry 360° synopsis, 2022 - 2035
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Platform
    • 2.2.3 Solutions
    • 2.2.4 Deployment Model
    • 2.2.5 Enterprise Size
    • 2.2.6 Application
  • 2.3 TAM Analysis, 2026-2035
  • 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Software-Defined Vehicles (SDVs) Adoption
      • 3.2.1.2 Growth of Autonomous Driving & ADAS
      • 3.2.1.3 Explosion of Connected Vehicle Data
      • 3.2.1.4 Shift Toward Cloud-Native Automotive Architectures
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 Data Security and Regulatory Compliance Challenges
      • 3.2.2.2 Integration Complexity with Legacy Automotive Systems
    • 3.2.3 Market opportunities
      • 3.2.3.1 Rise of Over-the-Air (OTA) Software Monetization Models
      • 3.2.3.2 Expansion of AI-Driven Predictive Maintenance and Vehicle Intelligence
      • 3.2.3.3 Growth of Digital Twins and Simulation-Based Development
      • 3.2.3.4 Increasing OEM-Hyperscaler Partnerships
  • 3.3 Growth potential analysis
  • 3.4 Technology and innovation landscape
    • 3.4.1 Current technological trends
    • 3.4.2 Emerging technologies
  • 3.5 Pricing Analysis (Driven by primary research)
    • 3.5.1 Historical Price Trend Analysis
    • 3.5.2 Pricing Strategy by Player Type
  • 3.6 Regulatory landscape
    • 3.6.1 North America
      • 3.6.1.1 National Highway Traffic Safety Administration (NHTSA)
      • 3.6.1.2 Federal Communications Commission (FCC)
      • 3.6.1.3 U.S. Department of Transportation (USDOT)
      • 3.6.1.4 Federal Trade Commission (FTC) Data Privacy Rules
      • 3.6.1.5 ISO/SAE 21434 Cybersecurity Standard
    • 3.6.2 Europe
      • 3.6.2.1 UNECE WP.29 (R155 & R156)
      • 3.6.2.2 General Data Protection Regulation (GDPR)
      • 3.6.2.3 EU Data Act
      • 3.6.2.4 European Union General Safety Regulation (GSR)
      • 3.6.2.5 ISO 26262 Functional Safety Standard
    • 3.6.3 Asia Pacific
      • 3.6.3.1 China Cybersecurity Law
      • 3.6.3.2 China Data Security Law
      • 3.6.3.3 China Personal Information Protection Law (PIPL)
      • 3.6.3.4 Japan Automotive Software & Mobility Safety Frameworks
      • 3.6.3.5 India Automotive Mission Plan (AMP)
    • 3.6.4 Latin America
      • 3.6.4.1 Brazil General Data Protection Law (LGPD)
      • 3.6.4.2 Mexico Automotive Digitalization & Data Governance Policies
      • 3.6.4.3 MERCOSUR Digital Integration Framework
      • 3.6.4.4 Chile Smart Mobility Regulations
    • 3.6.5 Middle East & Africa
      • 3.6.5.1 UAE Artificial Intelligence Strategy & Data Regulations
      • 3.6.5.2 Saudi Data & Artificial Intelligence Authority (SDAIA) Regulations
      • 3.6.5.3 GCC Digital Economy & Smart Mobility Framework
      • 3.6.5.4 African Union Digital Transformation Strategy
      • 3.6.5.5 African Continental Free Trade Area (AfCFTA) Digital Protocol
  • 3.7 Porter's analysis
  • 3.8 PESTEL analysis
  • 3.9 Patent analysis (Driven by primary research)
  • 3.10 Trade Data Analysis (Driven by paid database)
    • 3.10.1 Import/export volume & value trends
    • 3.10.2 Key trade corridors & tariff impact
  • 3.11 Cost breakdown analysis
  • 3.12 Impact of AI and Generative AI on the Market
    • 3.12.1 AI Driven Disruption of Existing Business Models
    • 3.12.2 GenAI Use Cases and Adoption Roadmap by Segment
    • 3.12.3 Risks Limitations and Regulatory Considerations
  • 3.13 Capacity & Production Landscape (Driven by Primary Research)
    • 3.13.1 Installed Capacity by Region & Key Producer
    • 3.13.2 Capacity Utilization Rates & Expansion Pipelines
  • 3.14 Sustainability and environmental aspects
    • 3.14.1 Sustainable practices
    • 3.14.2 Waste reduction strategies
    • 3.14.3 Energy efficiency in production
    • 3.14.4 Eco-friendly Initiatives
    • 3.14.5 Carbon footprint considerations
  • 3.15 Forecast assumptions & scenario analysis (Driven by Primary Research)
    • 3.15.1 Base Case- Key Macro & Industry Variables Driving CAGR
    • 3.15.2 Optimistic Scenarios- Favorable macro and industry tailwinds
    • 3.15.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 LATAM
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Key developments
    • 4.5.1 Mergers & acquisitions
    • 4.5.2 Partnerships & collaborations
    • 4.5.3 New Product Launches
    • 4.5.4 Expansion Plans and funding
  • 4.6 Company tier benchmarking
    • 4.6.1 Tier classification criteria & qualifying thresholds
    • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates & Forecast, By Platform, 2022 - 2035 (USD Mn)

  • 5.1 Key trends
  • 5.2 DevOps Platforms
  • 5.3 MLOps Platforms
  • 5.4 Unified DevOps-MLOps Platforms

Chapter 6 Market Estimates & Forecast, By Solutions, 2022 - 2035 (USD Mn)

  • 6.1 Key trends
  • 6.2 Software Platforms
  • 6.3 Infrastructure & Data Management Tools
  • 6.4 Services
    • 6.4.1 Professional Services
    • 6.4.2 Managed Services

Chapter 7 Market Estimates & Forecast, By Deployment Model, 2022 - 2035 (USD Mn)

  • 7.1 Key trends
  • 7.2 Public Cloud
  • 7.3 Private Cloud
  • 7.4 Hybrid Cloud

Chapter 8 Market Estimates & Forecast, By Enterprise Size, 2022 - 2035 (USD Mn)

  • 8.1 Key trends
  • 8.2 Large Enterprises
  • 8.3 Small & Medium Enterprises (SMEs)

Chapter 9 Market Estimates & Forecast, By Application, 2022 - 2035 (USD Mn)

  • 9.1 Key trends
  • 9.2 Vehicle autonomy & safety
  • 9.3 Connected vehicle services
  • 9.4 Fleet & asset management
  • 9.5 Predictive maintenance & reliability
  • 9.6 Manufacturing & supply chain analytics
  • 9.7 Other

Chapter 10 Market Estimates & Forecast, By Region, 2022 - 2035 (USD Mn)

  • 10.1 Key trends
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 France
    • 10.3.4 Italy
    • 10.3.5 Spain
    • 10.3.6 Russia
    • 10.3.7 Norway
    • 10.3.8 Netherlands
    • 10.3.9 Sweden
  • 10.4 Asia Pacific
    • 10.4.1 China
    • 10.4.2 India
    • 10.4.3 Japan
    • 10.4.4 Australia
    • 10.4.5 South Korea
    • 10.4.6 Singapore
    • 10.4.7 Thailand
    • 10.4.8 Indonesia
    • 10.4.9 Vietnam
  • 10.5 Latin America
    • 10.5.1 Brazil
    • 10.5.2 Mexico
    • 10.5.3 Argentina
  • 10.6 MEA
    • 10.6.1 South Africa
    • 10.6.2 Saudi Arabia
    • 10.6.3 UAE
    • 10.6.4 Turkey

Chapter 11 Company Profiles

  • 11.1 Global Players
    • 11.1.1 Amazon Web Services (AWS)
    • 11.1.2 Microsoft Azure
    • 11.1.3 Google Cloud
    • 11.1.4 IBM
    • 11.1.5 Oracle
    • 11.1.6 NVIDIA
    • 11.1.7 Databricks
    • 11.1.8 Snowflake
    • 11.1.9 SAP
    • 11.1.10 VMware (Broadcom)
    • 11.1.11 Palantir Technologies
    • 11.1.12 Siemens
    • 11.1.13 Cloudera
    • 11.1.14 Salesforce
    • 11.1.15 ServiceNow
    • 11.1.16 Atlassian
  • 11.2 Regional Players
    • 11.2.1 DataRobot
    • 11.2.2 H2O.ai
    • 11.2.3 SAS Institute
    • 11.2.4 Scale AI