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市場調查報告書
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1835490

機器學習即服務 (MLaaS) 市場按服務模式、使用類型、行業垂直、部署和組織規模分類 - 全球預測，2025 年至 2032 年

Machine-Learning-as-a-Service Market by Service Model, Application Type, Industry, Deployment, Organization Size - Global Forecast 2025-2032
出版日期: | 出版商: 360iResearch | 英文 195 Pages | 商品交期: 最快1-2個工作天內

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預計到 2032 年，機器學習即服務 (MLaaS) 市場規模將成長至 2,466.9 億美元，複合年成長率為 31.25%。

主要市場統計數據
基準年2024年 280億美元
預計2025年 366.8億美元
預測年份：2032年 2466.9億美元
複合年成長率（%） 31.25%

引領商品化運算、模型市場和 MLOps 整合的新時代，機器學習服務成為企業的核心功能。

機器學習即服務 (MLaaS) 已從實驗堆疊發展成為企業追求敏捷性、生產力和新收益的營運必需品。在過去幾年中，MLaaS 的技術組合已從客製化的本地建置轉變為整合預訓練模型、託管基礎設施和開發者工具的可組合服務。這種轉變將 ML 的應用範圍從資料科學專家擴展到應用開發和業務團隊，使他們能夠以遠低於傳統計劃所需開銷的成本整合 AI 功能。

因此，採購模式和供應商評估標準也發生了變化。除了原始模型效能之外，買家現在更優先考慮整合速度、模型管治和整體擁有成本。雲端原生供應商在託管服務和彈性運算方面展開競爭，而專業供應商則透過垂直化解決方案和特定領域模型進行差異化競爭。同時，開放原始碼基金會和社群主導的模型庫正在引入新的協作管道，以影響供應商的藍圖。

隨著企業尋求擴大生產級機器學習規模，諸如可觀察性、持續再訓練和安全特徵儲存等營運問題也隨之出現。在整個生命週期中管理模型的需求日益成長，這推動了 MLOps 學科的成熟，該學科將軟體工程實踐與資料管治相結合。這種對生命週期管理的務實關注使 MLaaS 不僅成為一個技術堆疊，更成為一種與企業風險、合規性和產品開發週期相交叉的營運能力。

摘要：商品化計算、標準化 API 和模型市場的引入，已將 MLaaS 從利基服務轉變為數位轉型的重要組成部分。決策者現在必須在速度和控制之間取得平衡，利用符合其策略目標的服務模型和部署選擇，同時確保機器學習營運的彈性、審核和成本效益。

認知到產業從基礎模型向混合部署和管治創新的關鍵轉變，這些轉變正在重新定義供應商的價值命題和買家的優先事項

一系列變革性變化正在重塑機器學習即服務 (MLaaS) 格局，這些變化將改變企業建構、採購和管理 AI 能力的方式。首先，大型底層模型和參數高效的微調技術的興起，正在加速自然語言處理和電腦視覺任務中實現最先進性能的進程。這種能力使高階 AI 更加普及，但也帶來了模型管治和一致性方面的挑戰，企業必須透過可解釋性、效能追蹤和防護措施來應對這些挑戰。

其次，邊緣運算和聯合方法的融合正在拓寬部署模式。需要低延遲、資料主權和降低出口成本的用例傾向於混合架構，將本地私有雲端雲或公共雲端的突發容量結合。這些混合模式需要一個能夠管理各種運行時，同時保持安全性和審核的編配層。

第三，商業和監管壓力迫使供應商在其託管產品中融入隱私保護技術和合規優先功能。差異化隱私、使用中加密和安全隔離區在敏感行業合約中變得越來越重要。在高度監管的行業中，能夠提供明確合約承諾和合規營運證據的供應商將擁有競爭優勢。

第四，透過成熟的 MLOps 實踐將機器學習投入營運，正在將投資重點從模型實驗轉向部署可靠性。用於資料檢驗、模型漂移檢測和可解釋性報告的自動化流程可以加快價值實現速度並降低業務風險。因此，提供整合可觀測性和生命週期工具的服務供應商可以取代單點解決方案。

最後，跨產業聯盟和垂直專業化正在改變市場動態。雲端服務供應商、晶片製造商和特定細分市場的軟體供應商之間的策略聯盟正在打造捆綁產品，從而減少最終客戶的整合摩擦。這些捆綁產品通常包含託管基礎架構、預先建置連接器和精選模型目錄，從而加速從概念驗證到運作的整個流程。這些轉變共同縮短了供應商評估週期，並重新定義了企業買家優先考慮的功能。

評估不斷變化的貿易政策和關稅動態將如何影響整個人工智慧基礎設施的硬體可用性、部署經濟性和採購彈性

美國將在2025年前實施關稅並調整貿易政策，將對機器學習基礎設施、籌資策略以及全球供應商關係產生連鎖影響。由於進口關稅和供應限制會改變成本結構和前置作業時間，機器學習堆疊中依賴硬體的元素，尤其是GPU和專用AI晶片等加速器，將成為焦點。依賴基於設備的內部部署解決方案或客製化硬體組件的公司將需要重新評估採購時間表、供應商管理的庫存安排以及軟體許可證以外的總實施成本。

同時，資費壓力可能會將依賴資本的​​內部部署模式轉變為營運支出模式，從而支援雲端優先策略。擁有分散式基礎設施和策略供應商關係的公共雲端供應商或許能夠減輕部分利潤率的影響，但客戶仍將透過調整定價和合約條款，或區域可用性限制感受到影響。然而，對資料駐留或主權有嚴格要求的組織可能會發現其工作負載遷移的靈活性有限，可能需要考慮私有雲端選項或混合拓撲，以平衡合規性和成本限制。

供應鏈彈性正逐漸成為採購風險管理的核心要素。那些在硬體方面採取多採購策略或利用某些供應商提供的軟著陸能力的公司，可以降低其受區域關稅變化影響的風險。那些追求垂直整合或本地組裝夥伴關係的公司也可以對沖進口造成的成本波動，但這些策略需要更長的前置作業時間和資本投入。

除了對硬體的直接影響外，關稅還將影響合作夥伴生態系統和打入市場策略。依賴國際零件供應鏈的供應商可能會加速區域夥伴關係，協商長期採購協議，並重新定價託管服務以保護利潤率。從商業性角度來看，採購和法律團隊可能會越來越嚴格地審查合約中有關不可抗力、關稅轉嫁和服務水準保證的條款。

簡而言之，關稅變化的累積影響將迫使企業對部署配置、採購條款和供應鏈應急計畫進行策略性重新評估。那些積極主動地模擬情境影響、多元化供應商關係，並擁有與監管和成本現實相符的部署架構的企業，即使在政策調整的情況下，也能保持其機器學習計畫的良好勢頭。

我們根據服務模型、應用程式專業化、產業、部署拓撲和組織規模來確定不同的需求，並據此制定策略提案。

細分分析揭示了不同服務模式、用例、垂直行業、部署選項和組織規模之間清晰的需求促進因素和營運限制。基於服務模式，提供者和購買者需要在三種產品之間做出權衡：強調彈性運算和託管硬體存取的基礎設施即服務 (IaaS)、捆綁開發工具和生命週期自動化的平台即服務 (PaaS) 解決方案，以及以最小的工程成本提供最終用戶功能的軟體即服務 (SaaS)。每種服務模式都針對不同的購買者角色和成熟度階段，因此，合約條款和支援模式的協調至關重要。

目錄

第1章：前言

第2章調查方法

第3章執行摘要

第4章 市場概況

第5章 市場洞察

  • 快速部署一個混合雲端部署中的模型管治和版本控制的 MLOps 平台
  • 低程式碼和無程式碼 MLaaS 解決方案的出現將使非技術業務用戶的模型開發變得民主化
  • 擴展預訓練基礎模型與可自訂微調功能的整合，以適應特定行業的用例
  • 專注於 MLaaS 平台內可解釋的 AI 功能，以滿足法規遵循和相關人員的透明度
  • 擴展邊緣 MLaaS 服務，支援在資源有限的設備上進行即時推理和分析
  • 整合人工智慧模型市場，無縫採購和使用第三方演算法和服務

第6章 2025年美國關稅的累積影響

第7章：人工智慧的累積影響，2025年

第 8 章：機器學習即服務 (MLaaS) 市場（按服務模式）

  • IaaS
  • PaaS
  • SaaS

第9章機器學習即服務（MLaaS）市場（按應用類型）

  • 電腦視覺
  • 自然語言處理
  • 預測分析
  • 建議引擎

第 10 章 機器學習即服務 (MLaaS) 市場（按垂直產業）

  • BFSI
  • 衛生保健
  • 資訊科技和通訊
  • 製造業
  • 零售

第 11 章 MLaaS（機器學習即服務）市場：按部署

  • 本地部署
    • 基於設備
    • 客製化解決方案
  • 私有雲端
    • IBM 雲端
    • OpenStack
    • VMware
  • 公共雲端
    • AWS
    • 谷歌雲端平台
    • Microsoft Azure

第 12 章 MLaaS（機器學習即服務）市場：依組織規模

  • 主要企業
  • 小型企業

第 13 章機器學習即服務 (MLaaS) 市場（按地區）

  • 美洲
    • 北美洲
    • 拉丁美洲
  • 歐洲、中東和非洲
    • 歐洲
    • 中東
    • 非洲
  • 亞太地區

第 14 章 機器學習即服務 (MLaaS) 市場（按類別）

  • ASEAN
  • GCC
  • EU
  • BRICS
  • G7
  • NATO

第 15 章 機器學習即服務 (MLaaS) 市場（按國家/地區）

  • 美國
  • 加拿大
  • 墨西哥
  • 巴西
  • 英國
  • 德國
  • 法國
  • 俄羅斯
  • 義大利
  • 西班牙
  • 中國
  • 印度
  • 日本
  • 澳洲
  • 韓國

第16章競爭格局

  • 2024年市佔率分析
  • 2024年FPNV定位矩陣
  • 競爭分析
    • Amazon.com, Inc.
    • Microsoft Corporation
    • Google LLC
    • Alibaba Group Holding Limited
    • International Business Machines Corporation
    • Oracle Corporation
    • Tencent Holdings Limited
    • Salesforce, Inc.
    • SAP SE
    • Baidu, Inc.
Product Code: MRR-43286DA08063

The Machine-Learning-as-a-Service Market is projected to grow by USD 246.69 billion at a CAGR of 31.25% by 2032.

KEY MARKET STATISTICS
Base Year [2024] USD 28.00 billion
Estimated Year [2025] USD 36.68 billion
Forecast Year [2032] USD 246.69 billion
CAGR (%) 31.25%

Navigating the new era where commoditized compute, model marketplaces, and MLOps converge to elevate machine-learning services into core enterprise capabilities

Machine-Learning-as-a-Service (MLaaS) has matured from an experimental stack into an operational imperative for organizations pursuing agility, productivity, and new revenue streams. Over the past several years the technology mix has shifted away from bespoke on-premises builds toward composable services that integrate pre-trained models, managed infrastructure, and developer tooling. This transition has expanded the pool of ML adopters beyond data science specialists to application developers and business teams who can embed AI capabilities with far lower overhead than traditional projects required.

Consequently, procurement patterns and vendor evaluation criteria have evolved. Buyers now weigh integration velocity, model governance, and total cost of ownership in addition to raw model performance. Cloud-native vendors compete on managed services and elastic compute, while specialized providers differentiate through verticalized solutions and domain-specific models. At the same time, open source foundations and community-driven model repositories have introduced new collaboration pathways that influence vendor roadmaps.

As organizations seek to scale production ML, operational concerns such as observability, continuous retraining, and secure feature stores have risen to prominence. The growing need to manage models across lifecycles has catalyzed a mature MLOps discipline that blends software engineering practices with data governance. This pragmatic focus on lifecycle management frames MLaaS not simply as a technology stack but as an operational capability that intersects enterprise risk, compliance, and product development cycles.

In summary, the introduction of commoditized compute, standardized APIs, and model marketplaces has transformed MLaaS from a niche offering into an essential enabler of digital transformation. Decision-makers must now balance speed with control, leveraging service models and deployment choices that align with strategic goals while ensuring resilient, auditable, and cost-effective ML operations.

Recognizing the pivotal industry shifts from foundation models to hybrid deployments and governance innovations that are redefining vendor value propositions and buyer priorities

The MLaaS landscape is being reshaped by a set of transformative shifts that collectively alter how businesses architect, procure, and govern AI capabilities. First, the rise of large foundation models and parameter-efficient fine-tuning techniques has accelerated access to state-of-the-art performance across natural language processing and computer vision tasks. This capability democratizes advanced AI but also introduces model governance and alignment challenges that enterprises must address through explainability, provenance tracking, and guardrails.

Second, the convergence of edge computing and federated approaches has broadened deployment patterns. Use cases that demand low latency, data sovereignty, or reduced egress costs favor hybrid architectures that blend on-premises appliances with private cloud and public cloud burst capacity. These hybrid patterns require orchestration layers that can manage diverse runtimes while preserving security and auditability.

Third, commercial and regulatory pressures are prompting vendors to embed privacy-preserving techniques and compliance-first features into managed offerings. Differential privacy, encryption-in-use, and secure enclaves are increasingly table stakes for contracts in sensitive industries. Vendors that provide clear contractual commitments and operational evidence of compliance gain a competitive advantage in highly regulated verticals.

Fourth, operationalization of ML through mature MLOps practices is shifting investment focus from model experimentation to deployment reliability. Automated pipelines for data validation, model drift detection, and explainability reporting reduce time-to-value and mitigate business risk. As a result, service providers that offer integrated observability and lifecycle tooling can displace point-solution approaches.

Lastly, industry partnerships and vertical specialization are changing go-to-market dynamics. Strategic alliances between cloud providers, chip manufacturers, and domain-specific software vendors create bundled offerings that lower integration friction for end customers. These bundles often include managed infrastructure, pre-built connectors, and curated model catalogs that accelerate proof of concept to production pathways. Together, these shifts compress vendor evaluation cycles and redefine the capabilities that enterprise buyers prioritize.

Assessing how shifts in trade policy and tariff dynamics influence hardware availability, deployment economics, and procurement resilience across AI infrastructure

The imposition of tariffs and trade policy adjustments in the United States during 2025 has cascading implications for ML infrastructure, procurement strategies, and global supplier relationships. Hardware-dependent elements of the ML stack-particularly accelerators such as GPUs and specialized AI silicon-become focal points when import duties or supply restrictions change cost structures and lead times. Enterprises reliant on appliance-based on-premises solutions or custom hardware assemblies must reassess procurement timelines, vendor-managed inventory arrangements, and the total cost of implementation beyond software licensing.

Simultaneously, tariff pressures can incentivize cloud-first strategies by shifting capital-dependent on-premises economics toward operational expenditure models. Public cloud providers with distributed infrastructure and strategic supplier relationships may be able to mitigate some margin impacts, but customers will still feel the effects through revised pricing, contract terms, or regional availability constraints. Organizations with strict data residency or sovereignty requirements, however, may have limited flexibility to move workloads and will need to explore private cloud options or hybrid topologies to reconcile compliance with cost constraints.

Supply chain resilience emerges as a core element of procurement risk management. Companies that maintain multi-sourcing strategies for hardware, or that leverage soft-landing capacities offered by certain vendors, reduce exposure to localized tariff changes. Firms that pursue vertical integration or local assembly partnerships can also create hedges against import-driven cost volatility, though these strategies require longer lead times and capital commitments.

Beyond direct hardware effects, tariffs influence partner ecosystems and go-to-market strategies. Vendors that depend on international component supply chains may accelerate regional partnerships, negotiate long-term purchase agreements, or reprice managed services to preserve margin. From a commercial standpoint, procurement and legal teams will increasingly scrutinize contract clauses related to force majeure, tariff pass-through, and service level assurances.

In short, the cumulative impact of tariff developments compels a strategic reassessment of deployment mix, procurement terms, and supply chain contingency planning. Organizations that proactively model scenario-based impacts, diversify supplier relationships, and align deployment architectures with regulatory and cost realities will be better positioned to sustain momentum in ML initiatives despite policy-induced disruptions.

Discerning differentiated demand across service models, application specializations, industry verticals, deployment topologies, and organization scale to tailor strategic offerings

Segmentation analysis reveals distinct demand drivers and operational constraints across service models, application types, industry verticals, deployment options, and organization size. Based on service model, providers and buyers navigate competing priorities among infrastructure-as-a-service offerings that emphasize elastic compute and managed hardware access, platform-as-a-service solutions that bundle development tooling and lifecycle automation, and software-as-a-service products that deliver end-user features with minimal engineering lift. Each service model appeals to different buyer personas and maturity stages, making alignment of contractual terms and support models essential.

Based on application type, the market is studied across computer vision, natural language processing, predictive analytics, and recommendation engines, each of which presents unique data requirements, latency expectations, and validation challenges. Computer vision workloads often demand specialized preprocessing and edge inference, while natural language processing applications require robust tokenization, prompt engineering, and continual domain adaptation. Predictive analytics emphasizes feature engineering and model explainability for decision support, and recommendation engines prioritize real-time scoring and privacy-aware personalization strategies.

Based on industry, the market is studied across banking, financial services and insurance, healthcare, information technology and telecom, manufacturing, and retail, where regulatory pressures, data sensitivity, and integration complexity differ markedly. Financial services and healthcare place a premium on auditability, explainability, and encryption, while manufacturing prioritizes real-time inference at the edge and integration with industrial control systems. Retail and telecom often focus on personalization and network-level optimization respectively, each demanding scalable feature pipelines and low-latency inference.

Based on deployment, the market is studied across on-premises, private cloud, and public cloud. On-premises implementations are further studied across appliance-based and custom solutions, reflecting the trade-offs between turnkey hardware-software stacks and bespoke configurations. Private cloud deployments are further studied across vendor-specific private platforms such as established enterprise-grade clouds and open-source driven stacks, while public cloud deployments are examined across major hyperscalers that offer managed AI services and global scale. These deployment distinctions influence procurement cycles, integration complexity, and operational ownership.

Based on organization size, the market is studied across large enterprises and small and medium enterprises, each with distinct buying behaviors and resource allocations. Large enterprises typically invest in tailored governance frameworks, hybrid architectures, and strategic vendor relationships, whereas small and medium enterprises often prioritize lower friction, subscription-based services that enable rapid experimentation and targeted feature adoption. Understanding these segmentation contours allows vendors to tailor product roadmaps and go-to-market motions that resonate with each buyer cohort.

Analyzing how regional regulatory environments, cloud ecosystems, and industrial priorities influence adoption pathways and deployment choices across global markets

Regional dynamics shape vendor strategies, regulatory expectations, and customer priorities in ways that materially affect adoption patterns and commercialization choices. In the Americas, there is a pronounced emphasis on rapid innovation cycles, a dense ecosystem of cloud service providers and start-ups, and strong demand for managed services that accelerate production deployments. North American buyers often seek vendor transparency on data governance and model provenance as they integrate AI into consumer-facing products and critical business processes.

Europe, the Middle East & Africa presents a mosaic of regulatory regimes and data sovereignty concerns that encourage private cloud and hybrid deployments. Organizations in this region place heightened emphasis on compliance capabilities, explainability, and localized data processing. Regulatory frameworks and sector-specific mandates influence procurement timelines and vendor selection criteria, prompting partnerships that prioritize certified infrastructure and demonstrable operational controls.

Asia-Pacific demonstrates wide variation between markets that favor rapid, cloud-centric adoption and those investing in local manufacturing and hardware capabilities. High-growth enterprise segments in this region often pursue ambitious digital initiatives that integrate ML with mobile-first experiences and industry-specific automation. Regional vendors and public cloud providers frequently localize offerings to address linguistic diversity, unique privacy regimes, and integration with domestic platforms. Across all regions, ecosystem relationships-spanning cloud providers, system integrators, and hardware suppliers-play a central role in enabling scalable deployments and localized support.

Exploring the competitive landscape where hyperscalers, specialized vendors, open source communities, and agile challengers converge to shape enterprise purchasing decisions

Competitive dynamics in the MLaaS sector reflect a blend of hyperscaler dominance, specialized vendors, open source initiatives, and emerging niche players. Leading cloud providers differentiate through integrated managed services, extensive infrastructure footprints, and partner ecosystems that reduce integration overhead for enterprise customers. These providers compete on SLA-backed services, compliance certifications, and the breadth of developer tooling available through their platforms.

Specialized vendors focus on verticalization, offering domain-specific models, curated datasets, and packaged integrations that address industry workflows. Their value proposition is grounded in deep domain expertise, faster time-to-value for industry use cases, and professional services that bridge the gap between proof of concept and production. Open source projects and model zoos continue to exert significant influence by shaping interoperability standards, accelerating innovation through community collaboration, and enabling cost-efficient experimentation for buyers and vendors alike.

Start-ups and challenger firms differentiate with edge-optimized inference engines, efficient parameter tuning solutions, or proprietary techniques for model compression and latency reduction. These firms attract customers requiring extreme performance or specific deployment constraints and often become acquisition targets for larger vendors seeking to augment their capabilities. Strategic alliances and M&A activity therefore remain central to the competitive landscape as incumbents shore up technology gaps and expand into adjacent verticals.

Enterprise procurement teams increasingly assess vendors on operational maturity, evidenced by robust lifecycle management, support for governance tooling, and transparent incident response protocols. Vendors that present clear roadmaps for interoperability, data portability, and ongoing model maintenance stand a better chance of securing long-term enterprise relationships. In this environment, trust, operational rigor, and the ability to demonstrate measurable business outcomes are decisive competitive differentiators.

Implementing strategic MLOps, procurement flexibility, privacy-first designs, and ecosystem partnerships to convert AI experiments into reliable enterprise outcomes

Industry leaders must adopt strategic measures that reconcile rapid innovation with reliable governance, resilient supply chains, and sustainable operational models. First, invest in robust MLOps foundations that prioritize reproducibility, continuous validation, and model observability. Establishing automated pipelines for data quality checks, drift detection, and explainability reporting reduces operational risk and accelerates safe deployment of models into revenue-generating applications.

Second, align procurement strategies with deployment flexibility by negotiating contracts that allow hybrid topologies and multi-cloud portability. Including clauses for tariff pass-through mitigation, supplier diversification, and localized support enables organizations to adapt to policy shifts while preserving operational continuity. Scenario planning that models the implications of hardware supply constraints and price variability will help legal and procurement teams secure more resilient terms.

Third, prioritize privacy-preserving architectures and compliance-first features in vendor selection criteria. Implementing privacy-enhancing technologies and embedding audit trails into model lifecycles not only addresses regulatory demands but also builds customer trust. Operationalizing ethical review processes and risk assessment frameworks ensures new models are evaluated for fairness, security, and business alignment before deployment.

Fourth, cultivate ecosystem partnerships to bolster capabilities that are not core to the business. Collaborating with systems integrators, domain-specialist vendors, and academic labs can accelerate access to curated datasets and niche modeling techniques. These partnerships should be governed by clear IP, data sharing, and commercial terms to avoid downstream disputes.

Finally, invest in talent and change management programs that translate technical capability into business impact. Cross-functional teams that combine product managers, data engineers, and compliance leaders are more effective at operationalizing AI initiatives. Equipping these teams with accessible tooling and executive-level dashboards fosters accountability and aligns ML outcomes with strategic objectives.

Applying a mixed-methods research framework that combines expert interviews, technical literature review, and cross-segmentation analysis to ensure rigorous and actionable insights

This research synthesizes primary and secondary inputs to create a rigorous, reproducible framework for analyzing MLaaS dynamics. The primary research component comprises structured interviews with technical leaders, procurement professionals, and domain specialists to validate vendor capabilities, operational practices, and deployment preferences. These qualitative engagements provide real-world context that informs segmentation treatment and scenario-based analysis.

Secondary research involves systematic review of public filings, vendor whitepapers, regulatory guidance, and academic publications to triangulate technology trends and governance developments. Emphasis is placed on technical documentation and reproducible research that illuminate algorithmic advances, deployment patterns, and interoperability standards. Market signals such as partnership announcements, major product launches, and industry consortium activity are evaluated for their strategic implications.

Analysis techniques include cross-segmentation mapping to reveal how service models interact with application requirements and deployment choices, as well as sensitivity analysis to assess the operational impact of supply chain and policy changes. Findings are validated through iterative workshops with subject-matter experts to ensure practical relevance and to refine recommendations. Wherever possible, methodologies include transparent assumptions and traceable evidence trails to support executive decision-making.

The overall approach balances technical depth with commercial applicability, emphasizing actionable insights rather than raw technical minutiae. This ensures that the outputs are accessible to both engineering leaders and senior executives responsible for procurement, compliance, and strategic planning.

Concluding that sustainable MLaaS adoption requires operational rigor, hybrid deployment strategies, and governance to convert advanced models into reliable business value

Machine-Learning-as-a-Service stands at an inflection point where technological possibility meets operational pragmatism. The current landscape demands a balanced approach that embraces powerful model capabilities while instituting the controls necessary to manage risk, cost, and regulatory obligations. Organizations that succeed will be those that treat MLaaS as an enterprise capability requiring cross-functional governance, supply chain resilience, and clear metrics for business impact.

Strategic choices around service model, deployment topology, and vendor selection will determine the pace at which organizations convert experimentation into production outcomes. Hybrid architectures that combine the scalability of public cloud with the control of private environments offer a pragmatic path for regulated industries and latency-sensitive applications. Meanwhile, advances in model efficiency, federated learning, and privacy-enhancing technologies create new opportunities to reconcile data protection with innovation.

Ultimately, sustainable adoption of MLaaS depends on institutionalizing MLOps practices, cultivating partnerships that extend core competencies, and embedding compliance into the development lifecycle. Leaders who invest in these areas will be better positioned to capture the productivity and strategic advantages that machine learning enables, while minimizing exposure to policy shifts and supply chain disruptions.

Table of Contents

1. Preface

  • 1.1. Objectives of the Study
  • 1.2. Market Segmentation & Coverage
  • 1.3. Years Considered for the Study
  • 1.4. Currency & Pricing
  • 1.5. Language
  • 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

  • 5.1. Rapid adoption of MLOps platforms integrating model governance and version control across hybrid cloud deployments
  • 5.2. Emergence of low-code and no-code MLaaS solutions democratizing model development among nontechnical business users
  • 5.3. Growing integration of pre-trained foundation models with customizable fine-tuning for industry-specific use cases
  • 5.4. Increased focus on explainable AI features within MLaaS platforms to satisfy regulatory compliance and stakeholder transparency
  • 5.5. Expansion of edge MLaaS offerings enabling real-time inference and analytics on resource-constrained devices
  • 5.6. Integration of AI model marketplaces for seamless procurement and consumption of third-party algorithms and services

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Machine-Learning-as-a-Service Market, by Service Model

  • 8.1. Iaas
  • 8.2. Paas
  • 8.3. Saas

9. Machine-Learning-as-a-Service Market, by Application Type

  • 9.1. Computer Vision
  • 9.2. Natural Language Processing
  • 9.3. Predictive Analytics
  • 9.4. Recommendation Engines

10. Machine-Learning-as-a-Service Market, by Industry

  • 10.1. BFSI
  • 10.2. Healthcare
  • 10.3. IT And Telecom
  • 10.4. Manufacturing
  • 10.5. Retail

11. Machine-Learning-as-a-Service Market, by Deployment

  • 11.1. On-Premises
    • 11.1.1. Appliance Based
    • 11.1.2. Custom Solutions
  • 11.2. Private Cloud
    • 11.2.1. Ibm Cloud
    • 11.2.2. Openstack
    • 11.2.3. Vmware
  • 11.3. Public Cloud
    • 11.3.1. Aws
    • 11.3.2. Google Cloud Platform
    • 11.3.3. Microsoft Azure

12. Machine-Learning-as-a-Service Market, by Organization Size

  • 12.1. Large Enterprise
  • 12.2. Small And Medium Enterprises

13. Machine-Learning-as-a-Service Market, by Region

  • 13.1. Americas
    • 13.1.1. North America
    • 13.1.2. Latin America
  • 13.2. Europe, Middle East & Africa
    • 13.2.1. Europe
    • 13.2.2. Middle East
    • 13.2.3. Africa
  • 13.3. Asia-Pacific

14. Machine-Learning-as-a-Service Market, by Group

  • 14.1. ASEAN
  • 14.2. GCC
  • 14.3. European Union
  • 14.4. BRICS
  • 14.5. G7
  • 14.6. NATO

15. Machine-Learning-as-a-Service Market, by Country

  • 15.1. United States
  • 15.2. Canada
  • 15.3. Mexico
  • 15.4. Brazil
  • 15.5. United Kingdom
  • 15.6. Germany
  • 15.7. France
  • 15.8. Russia
  • 15.9. Italy
  • 15.10. Spain
  • 15.11. China
  • 15.12. India
  • 15.13. Japan
  • 15.14. Australia
  • 15.15. South Korea

16. Competitive Landscape

  • 16.1. Market Share Analysis, 2024
  • 16.2. FPNV Positioning Matrix, 2024
  • 16.3. Competitive Analysis
    • 16.3.1. Amazon.com, Inc.
    • 16.3.2. Microsoft Corporation
    • 16.3.3. Google LLC
    • 16.3.4. Alibaba Group Holding Limited
    • 16.3.5. International Business Machines Corporation
    • 16.3.6. Oracle Corporation
    • 16.3.7. Tencent Holdings Limited
    • 16.3.8. Salesforce, Inc.
    • 16.3.9. SAP SE
    • 16.3.10. Baidu, Inc.

LIST OF FIGURES

  • FIGURE 1. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2018-2032 (USD MILLION)
  • FIGURE 2. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2024 VS 2032 (%)
  • FIGURE 3. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 4. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2024 VS 2032 (%)
  • FIGURE 5. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 6. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2024 VS 2032 (%)
  • FIGURE 7. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 8. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2024 VS 2032 (%)
  • FIGURE 9. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 10. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2024 VS 2032 (%)
  • FIGURE 11. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 12. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 13. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 14. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 15. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 16. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 17. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 18. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 19. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 20. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 21. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GROUP, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 22. ASEAN MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 23. GCC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 24. EUROPEAN UNION MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 25. BRICS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 26. G7 MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 27. NATO MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 28. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
  • FIGURE 29. MACHINE-LEARNING-AS-A-SERVICE MARKET SHARE, BY KEY PLAYER, 2024
  • FIGURE 30. MACHINE-LEARNING-AS-A-SERVICE MARKET, FPNV POSITIONING MATRIX, 2024

LIST OF TABLES

  • TABLE 1. MACHINE-LEARNING-AS-A-SERVICE MARKET SEGMENTATION & COVERAGE
  • TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
  • TABLE 3. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2018-2024 (USD MILLION)
  • TABLE 4. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, 2025-2032 (USD MILLION)
  • TABLE 5. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 6. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 7. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IAAS, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 8. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IAAS, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 9. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IAAS, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 10. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IAAS, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 11. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IAAS, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 12. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IAAS, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 13. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PAAS, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 14. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PAAS, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 15. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PAAS, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 16. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PAAS, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 17. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PAAS, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 18. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PAAS, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 19. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SAAS, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 20. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SAAS, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 21. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SAAS, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 22. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SAAS, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 23. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SAAS, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 24. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SAAS, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 25. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 26. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 27. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPUTER VISION, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 28. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPUTER VISION, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 29. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 30. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 31. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 32. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 33. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 34. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 35. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 36. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 37. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 38. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 39. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 40. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 41. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 42. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 43. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 44. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PREDICTIVE ANALYTICS, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 45. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RECOMMENDATION ENGINES, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 46. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RECOMMENDATION ENGINES, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 47. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RECOMMENDATION ENGINES, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 48. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RECOMMENDATION ENGINES, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 49. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RECOMMENDATION ENGINES, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 50. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RECOMMENDATION ENGINES, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 51. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 52. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 53. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 54. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 55. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 56. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 57. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 58. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY BFSI, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 59. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 60. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 61. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 62. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 63. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 64. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY HEALTHCARE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 65. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IT AND TELECOM, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 66. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IT AND TELECOM, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 67. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IT AND TELECOM, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 68. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IT AND TELECOM, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 69. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IT AND TELECOM, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 70. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IT AND TELECOM, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 71. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 72. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 73. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 74. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 75. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 76. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MANUFACTURING, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 77. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 78. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 79. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 80. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 81. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 82. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY RETAIL, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 83. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 84. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 85. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 86. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 87. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 88. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 89. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 90. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 91. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 92. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 93. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLIANCE BASED, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 94. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLIANCE BASED, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 95. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLIANCE BASED, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 96. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLIANCE BASED, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 97. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLIANCE BASED, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 98. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLIANCE BASED, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 99. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY CUSTOM SOLUTIONS, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 100. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY CUSTOM SOLUTIONS, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 101. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY CUSTOM SOLUTIONS, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 102. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY CUSTOM SOLUTIONS, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 103. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY CUSTOM SOLUTIONS, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 104. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY CUSTOM SOLUTIONS, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 105. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 106. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 107. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 108. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 109. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 110. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 111. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 112. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 113. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IBM CLOUD, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 114. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IBM CLOUD, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 115. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IBM CLOUD, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 116. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IBM CLOUD, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 117. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IBM CLOUD, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 118. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY IBM CLOUD, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 119. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY OPENSTACK, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 120. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY OPENSTACK, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 121. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY OPENSTACK, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 122. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY OPENSTACK, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 123. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY OPENSTACK, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 124. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY OPENSTACK, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 125. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY VMWARE, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 126. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY VMWARE, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 127. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY VMWARE, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 128. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY VMWARE, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 129. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY VMWARE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 130. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY VMWARE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 131. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 132. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 133. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 134. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 135. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 136. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 137. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 138. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 139. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AWS, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 140. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AWS, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 141. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AWS, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 142. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AWS, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 143. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AWS, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 144. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY AWS, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 145. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GOOGLE CLOUD PLATFORM, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 146. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GOOGLE CLOUD PLATFORM, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 147. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GOOGLE CLOUD PLATFORM, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 148. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GOOGLE CLOUD PLATFORM, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 149. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GOOGLE CLOUD PLATFORM, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 150. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY GOOGLE CLOUD PLATFORM, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 151. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MICROSOFT AZURE, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 152. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MICROSOFT AZURE, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 153. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MICROSOFT AZURE, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 154. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MICROSOFT AZURE, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 155. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MICROSOFT AZURE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 156. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY MICROSOFT AZURE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 157. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 158. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 159. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY LARGE ENTERPRISE, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 160. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY LARGE ENTERPRISE, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 161. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY LARGE ENTERPRISE, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 162. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY LARGE ENTERPRISE, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 163. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY LARGE ENTERPRISE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 164. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY LARGE ENTERPRISE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 165. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SMALL AND MEDIUM ENTERPRISES, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 166. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SMALL AND MEDIUM ENTERPRISES, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 167. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SMALL AND MEDIUM ENTERPRISES, BY GROUP, 2018-2024 (USD MILLION)
  • TABLE 168. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SMALL AND MEDIUM ENTERPRISES, BY GROUP, 2025-2032 (USD MILLION)
  • TABLE 169. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SMALL AND MEDIUM ENTERPRISES, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 170. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SMALL AND MEDIUM ENTERPRISES, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 171. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2018-2024 (USD MILLION)
  • TABLE 172. GLOBAL MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY REGION, 2025-2032 (USD MILLION)
  • TABLE 173. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SUBREGION, 2018-2024 (USD MILLION)
  • TABLE 174. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SUBREGION, 2025-2032 (USD MILLION)
  • TABLE 175. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 176. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 177. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 178. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 179. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 180. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 181. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 182. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 183. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 184. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 185. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 186. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 187. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 188. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 189. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 190. AMERICAS MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 191. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 192. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 193. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 194. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 195. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 196. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 197. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 198. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 199. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 200. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 201. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 202. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 203. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 204. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 205. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 206. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 207. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 208. NORTH AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 209. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 210. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 211. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 212. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 213. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 214. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 215. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 216. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 217. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 218. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 219. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 220. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 221. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 222. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 223. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 224. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 225. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 226. LATIN AMERICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 227. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SUBREGION, 2018-2024 (USD MILLION)
  • TABLE 228. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SUBREGION, 2025-2032 (USD MILLION)
  • TABLE 229. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 230. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 231. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 232. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 233. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 234. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 235. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 236. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 237. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 238. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 239. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 240. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 241. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 242. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 243. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 244. EUROPE, MIDDLE EAST & AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 245. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 246. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 247. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 248. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 249. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 250. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 251. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 252. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 253. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 254. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 255. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 256. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 257. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 258. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 259. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 260. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 261. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 262. EUROPE MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 263. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 264. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 265. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 266. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 267. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 268. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 269. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 270. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 271. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 272. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 273. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 274. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 275. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 276. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 277. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 278. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 279. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 280. MIDDLE EAST MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 281. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2018-2024 (USD MILLION)
  • TABLE 282. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY COUNTRY, 2025-2032 (USD MILLION)
  • TABLE 283. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2018-2024 (USD MILLION)
  • TABLE 284. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY SERVICE MODEL, 2025-2032 (USD MILLION)
  • TABLE 285. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2018-2024 (USD MILLION)
  • TABLE 286. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY APPLICATION TYPE, 2025-2032 (USD MILLION)
  • TABLE 287. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2018-2024 (USD MILLION)
  • TABLE 288. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY INDUSTRY, 2025-2032 (USD MILLION)
  • TABLE 289. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2018-2024 (USD MILLION)
  • TABLE 290. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY DEPLOYMENT, 2025-2032 (USD MILLION)
  • TABLE 291. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2018-2024 (USD MILLION)
  • TABLE 292. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ON-PREMISES, 2025-2032 (USD MILLION)
  • TABLE 293. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2018-2024 (USD MILLION)
  • TABLE 294. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PRIVATE CLOUD, 2025-2032 (USD MILLION)
  • TABLE 295. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2018-2024 (USD MILLION)
  • TABLE 296. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY PUBLIC CLOUD, 2025-2032 (USD MILLION)
  • TABLE 297. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2018-2024 (USD MILLION)
  • TABLE 298. AFRICA MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY ORGANIZATION SIZE, 2025-2032 (USD MILLION)
  • TABLE 299. ASIA-PACIFIC MACHINE-LEARNING-AS-A-SERVICE MARKET SIZE, BY