机器学习即服务 (MLaaS) 市场按服务模式、使用类型、行业垂直、部署和组织规模划分 - 全球预测，2025 年至 2032 年

预计到 2032 年，机器学习即服务 (MLaaS) 市场规模将成长至 2,466.9 亿美元，复合年增长率为 31.25%。

主要市场统计数据
基准年2024年	280亿美元
预计2025年	366.8亿美元
预测年份：2032年	2466.9亿美元
复合年增长率（%）	31.25%

引领商品化运算、模型市场和 MLOps 整合的新时代，机器学习服务成为企业的核心功能。

机器学习即服务 (MLaaS) 已从实验堆迭发展成为企业追求敏捷性、生产力和新收益的营运必需品。在过去几年中，MLaaS 的技术组合已从客製化的本地建置转变为整合预训练模型、託管基础设施和开发者工具的可组合服务。这种转变将 ML 的应用范围从资料科学专家扩展到应用开发和业务团队，使他们能够以远低于传统计划所需开销的成本整合 AI 功能。

因此，采购模式和供应商评估标准也发生了变化。除了原始模型效能之外，买家现在更优先考虑整合速度、模型管治和整体拥有成本。云端原生供应商在託管服务和弹性运算方面展开竞争，而专业供应商则透过垂直化解决方案和特定领域模型进行差异化竞争。同时，开放原始码基金会和社群主导的模型库正在引入新的协作管道，以影响供应商的蓝图。

随着企业寻求扩大生产级机器学习规模，诸如可观察性、持续再训练和安全特征储存等营运问题也随之出现。在整个生命週期中管理模型的需求日益增长，这推动了 MLOps 学科的成熟，该学科将软体工程实践与资料管治相结合。这种对生命週期管理的务实关注使 MLaaS 不仅成为一个技术堆迭，更成为一种与企业风险、合规性和产品开发週期相交叉的营运能力。

摘要：商品化计算、标准化 API 和模型市场的引入，已将 MLaaS 从利基服务转变为数位转型的重要组成部分。决策者现在必须在速度和控制之间取得平衡，利用符合其策略目标的服务模型和部署选择，同时确保机器学习营运的弹性、审核和成本效益。

认识到产业从基础模型向混合部署和管治创新的关键转变，这些转变正在重新定义供应商的价值命题和买家的优先事项

一系列变革性变化正在重塑机器学习即服务 (MLaaS) 格局，这些变化将改变企业建构、采购和管理 AI 能力的方式。首先，大型底层模型和参数高效的微调技术的兴起，正在加速自然语言处理和电脑视觉任务中实现最先进性能的进程。这种能力使高阶 AI 更加普及，但也带来了模型管治和一致性方面的挑战，企业必须透过可解释性、效能追踪和防护措施来应对这些挑战。

其次，边缘运算和联合方法的融合正在拓宽部署模式。需要低延迟、资料主权和降低出口成本的用例倾向于混合架构，将本地私有云端云或公共云端的突发容量结合。这些混合模式需要一个能够管理各种运行时，同时保持安全性和审核的编配层。

第三，商业和监管压力迫使供应商在其託管产品中融入隐私保护技术和合规优先功能。差异化隐私、使用中加密和安全隔离区在敏感行业合约中变得越来越重要。在高度监管的行业中，能够提供明确合约承诺和合规营运证据的供应商将拥有竞争优势。

第四，透过成熟的 MLOps 实践将机器学习投入运营，正在将投资重点从模型实验转向部署可靠性。用于资料检验、模型漂移检测和可解释性报告的自动化流程可以加快价值实现速度并降低业务风险。因此，提供整合可观测性和生命週期工具的服务供应商可以取代单点解决方案。

最后，跨产业联盟和垂直专业化正在改变市场动态。云端服务供应商、晶片製造商和特定细分市场的软体供应商之间的策略联盟正在打造捆绑产品，从而减少最终客户的整合摩擦。这些捆绑产品通常包含託管基础架构、预先建置连接器和精选模型目录，从而加速从概念验证到运作的整个流程。这些转变共同缩短了供应商评估週期，并重新定义了企业买家优先考虑的功能。

评估不断变化的贸易政策和关税动态将如何影响整个人工智慧基础设施的硬体可用性、部署经济性和采购弹性

美国将在2025年前实施关税并调整贸易政策，将对机器学习基础设施、筹资策略以及全球供应商关係产生连锁影响。由于进口关税和供应限制会改变成本结构和前置作业时间，机器学习堆迭中依赖硬体的元素，尤其是GPU和专用AI晶片等加速器，将成为焦点。依赖基于设备的内部部署解决方案或客製化硬体组件的公司将需要重新评估采购时间表、供应商管理的库存安排以及软体许可证以外的总实施成本。

同时，资费压力可能会将依赖资本的​​内部部署模式转变为营运支出模式，从而支援云端优先策略。拥有分散式基础设施和策略供应商关係的公共云端供应商或许能够减轻部分利润率的影响，但客户仍将透过调整定价和合约条款，或区域可用性限制感受到影响。然而，对资料驻留或主权有严格要求的组织可能会发现其工作负载迁移的灵活性有限，可能需要考虑私有云端选项或混合拓扑，以平衡合规性和成本限制。

供应链弹性正逐渐成为采购风险管理的核心要素。那些在硬体方面采取多采购策略或利用某些供应商提供的软着陆能力的公司，可以降低其受区域关税变化影响的风险。那些追求垂直整合或本地组装伙伴关係的公司也可以对冲进口造成的成本波动，但这些策略需要更长的前置作业时间和资本投入。

除了对硬体的直接影响外，关税还将影响合作伙伴生态系统和打入市场策略。依赖国际零件供应链的供应商可能会加速区域伙伴关係，协商长期采购协议，并重新定价託管服务以保护利润率。从商业性角度来看，采购和法律团队可能会越来越严格地审查合约中有关不可抗力、关税转嫁和服务水准保证的条款。

简而言之，关税变化的累积影响将迫使企业对部署配置、采购条款和供应链应急计画进行策略性重新评估。那些积极主动地模拟情境影响、多元化供应商关係，并拥有与监管和成本现实相符的部署架构的企业，即使在政策调整的情况下，也能保持其机器学习计画的良好势头。

我们根据服务模型、应用程式专业化、产业、部署拓扑和组织规模来确定不同的需求，并据此制定策略提案。

細項分析揭示了不同服务模式、用例、垂直行业、部署选项和组织规模之间清晰的需求驱动因素和营运限制。基于服务模式，提供者和购买者需要在三种产品之间做出权衡：强调弹性运算和託管硬体存取的基础设施即服务 (IaaS)、捆绑开发工具和生命週期自动化的平台即服务 (PaaS) 解决方案，以及以最小的工程成本提供最终用户功能的软体即服务 (SaaS)。每种服务模式都针对不同的购买者角色和成熟度阶段，因此，合约条款和支援模式的协调至关重要。

目录

第一章：前言

第二章调查方法

第三章执行摘要

第四章 市场概况

第五章 市场洞察

• 快速部署一个混合云端部署中的模型管治和版本控制的 MLOps 平台
• 低程式码和无程式码 MLaaS 解决方案的出现将使非技术业务用户的模型开发变得民主化
• 扩展预训练基础模型与可自订微调功能的集成，以适应特定行业的用例
• 专注于 MLaaS 平台内可解释的 AI 功能，以满足法规遵循和相关人员的透明度
• 扩展边缘 MLaaS 服务，支援在资源有限的设备上进行即时推理和分析
• 整合人工智慧模型市场，无缝采购和使用第三方演算法和服务

第六章 2025年美国关税的累积影响

第七章：人工智慧的累积影响，2025年

第 8 章：机器学习即服务 (MLaaS) 市场（按服务模式）

• IaaS
• PaaS
• SaaS

第九章机器学习即服务（MLaaS）市场（按应用类型）

• 电脑视觉
• 自然语言处理
• 预测分析
• 建议引擎

第 10 章 机器学习即服务 (MLaaS) 市场（按垂直产业）

• BFSI
• 卫生保健
• 资讯科技和通讯
• 製造业
• 零售

第 11 章 MLaaS（机器学习即服务）市场：按部署

• 本地部署
  • 基于设备
  • 客製化解决方案
• 私有云端
  • IBM 云端
  • OpenStack
  • VMware
• 公共云端
  • AWS
  • 谷歌云端平台
  • Microsoft Azure

第 12 章 MLaaS（机器学习即服务）市场：依组织规模

• 大公司
• 小型企业

第 13 章机器学习即服务 (MLaaS) 市场（按地区）

• 美洲
  • 北美洲
  • 拉丁美洲
• 欧洲、中东和非洲
  • 欧洲
  • 中东
  • 非洲
• 亚太地区

第 14 章 机器学习即服务 (MLaaS) 市场（按类别）

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

第 15 章 机器学习即服务 (MLaaS) 市场（按国家/地区）

• 美国
• 加拿大
• 墨西哥
• 巴西
• 英国
• 德国
• 法国
• 俄罗斯
• 义大利
• 西班牙
• 中国
• 印度
• 日本
• 澳洲
• 韩国

第十六章竞争格局

• 2024年市占率分析
• 2024年FPNV定位矩阵
• 竞争分析
  • 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.

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