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市场调查报告书
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1871867

全球人工智慧模型训练市场：预测至 2032 年—按训练类型、部署方式、技术、应用、最终用户和地区进行分析

AI Model Training Market Forecasts to 2032 - Global Analysis By Training Type, Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球人工智慧模型训练市场价值将达到 171.5 亿美元，到 2032 年将达到 1,249.2 亿美元，在预测期内的复合年增长率为 32.8%。

人工智慧模型训练是指系统从资料中学习并逐步获得决策能力的开发阶段。这个过程始于收集可靠的资料集，对其进行清洗，并将其准备好输入到选定的学习框架中。透过训练，模型会调整其内部权重以减少误差并提高预测精度。根据目标的不同，团队可以应用监督式学习、无监督学习或强化学习方法，并辅以优化策略来提升学习效率。效能监控透过测试样本和准确率指标进行，以防止过度拟合等问题。更强大的处理器和大规模的资料池使训练更加动态，从而支援跨多个行业的高级应用和更深入的洞察。

根据艾伦人工智慧研究所 (AI2) 的说法，语义学者开放研究语料库包含超过 2 亿篇学术文章，其中许多文章被用于训练科学和生物医学领域的人工智慧模型。

巨量资料分析日益普及

人工智慧模型训练市场的主要成长要素是巨量资料分析的快速发展。企业正从社群媒体、物联网设备、软体应用和营运系统产生大量资料流。为了有效利用这些讯息，企业正在采用能够有效处理大型资料集的训练平台。这些模型能够支援进阶预测、自动化和个人化客户体验。日益增长的资料多样性正在推动对高效能云端运算和基于GPU的运算的投资，以加速训练週期。随着即时资料洞察成为竞争优势的关键，企业正依靠强大的AI训练将原始资讯转化为策略洞察，从而改善业务成果并做出更明智的决策。

高昂的计算成本和基础设施限制

人工智慧模型训练市场面临的一大挑战是，大规模训练所需的运算系统高成本。复杂的神经网路需要高效能GPU、强大的处理器和高频宽的云端资源，这些资源的购买和营运成本都非常高。中小企业和教育机构面临预算限制，减缓了人工智慧的普及。电力和冷却需求进一步增加了营运成本，尤其是在持续训练的情况下。漫长的处理时间也延缓了新模型的测试和部署。因此，一些公司被迫缩减人工智慧计划的规模，或选择轻量级架构。整体而言，沉重的财务负担阻碍了人工智慧的发展，尤其对于那些缺乏先进基础设施的组织而言更是如此。

边缘人工智慧和设备内模型训练的成长

边缘运算透过将学习功能从集中式云端系统转移到本地设备，为人工智慧模型训练市场创造了巨大的机会。直接在硬体上运行训练过程可以减少资料传输，提高反应速度，并提供更强大的隐私保护。紧凑型神经网路模型、优化处理器和联邦学习技术的进步，使得在物联网设备、机器人、联网汽车和行动电话等设备上进行演算法更新和改进成为可能。各行各业都能从中受益，获得即时洞察、持续智慧和更低的云端依赖性。这种方法可以减少网路过载，即使在网路连接不佳的环境中也能支援可靠的人工智慧效能，因此，基于边缘的训练已成为包括交通运输、製造业、医疗保健和智慧城市应用在内的众多行业的热门选择。

科技快速过时和竞争压力

人工智慧技术的快速创新对人工智慧模型训练市场构成重大威胁。新的硬体、架构和学习方法层出不穷，缩短了现有模型的寿命。为了保持竞争力，企业被迫频繁地修改和重新训练系统，这增加了成本和营运复杂性。资源雄厚的大公司创新速度更快，使规模较小的竞争对手处于劣势。频繁的技术更迭拖慢了计划週期，并为投资报酬率（ROI）带来了不确定性。由于工具快速过时，许多公司难以选择长期策略。因此，市场面临激烈的竞争、不稳定以及资源受限企业采用率降低的风险。

新冠疫情的影响：

新冠疫情对人工智慧模型训练市场产生了正面和负面的双重影响。许多企业迅速转向数位化运营，推动了对云端平台、自动化工作流程和智慧分析的需求。这种转变促使企业增加对人工智慧训练的投资，尤其是在线上零售、远端医疗、银行和供应链服务等领域。同时，经济的不确定性和技术预算的缩减减缓了中小企业采用人工智慧的速度。远距办公环境推动了虚拟训练基础架构和基于订阅的人工智慧开发模式的应用。医疗研究、远端监控和安全应用领域对人工智慧的日益依赖也加速了创新。儘管疫情带来了许多挑战，但最终还是巩固了人工智慧训练技术的长期成长及其策略重要性。

在预测期内，云端基础市场将占据最大的市场份额。

预计在预测期内，云端基础的细分市场将占据最大的市场份额，因为它提供了无与伦比的灵活性、速度和扩充性。企业无需购买昂贵的硬件，而是依靠弹性云资源进行资料处理、储存和高效能GPU。这使得团队能够更快地建置、重新训练和部署模型，同时控制营运成本。云端平台包含自动化管道、预先配置工具和分散式运算功能，从而提高生产力并缩短计划週期。远端办公环境提供了无缝存取和协作开发的优势。随着人们对深度学习、预测分析和智慧自动化的兴趣日益浓厚，云端采用透过提供高效、安全且易于扩展的AI训练环境，继续保持其主导地位，该环境适用于各种规模的组织。

在预测期内，医疗保健产业将实现最高的复合年增长率。

预计在预测期内，医疗保健产业将呈现最高的成长率，因为医疗机构正在迅速采用先进的数据驱动系统。人工智慧模型正被训练用于诊断影像分析、精准医疗、药物研发和自动化决策支援。医院和研究机构依靠强大的训练基础设施来分析复杂的患者资料集，并提供更快、更可靠的结果。远端医疗、智慧医疗设备、生物感测器和基因研究的扩展正在推动对人工智慧演算法持续改进的需求。这些模型有助于疾病的早期检测，并有助于制定更精准的治疗方案。随着数位转型在全球医疗保健生态系统中不断扩展，对经过专业训练的医疗人工智慧工具的需求正在以最快的速度成长。

占比最大的地区：

预计北美将在预测期内占据最大的市场份额，这得益于其完善的人工智慧生态系统、对创新的大力投入以及众多顶尖科技公司的集中。北美拥有卓越的运算基础设施、充裕的财政资源以及在模型开发和训练方面经验丰富的庞大人才库。该地区的医疗保健、银行和自动驾驶汽车等行业正在积极采用并改善复杂的人工智慧系统。在该地区运营的大规模云端服务和人工智慧服务供应商提供快速运算和对大量资料集的无缝存取。这些优势的综合作用将使北美在所有行业的人工智慧模型训练市场中占据最大的份额。

预计年复合成长率最高的地区：

亚太地区预计将在预测期内实现最高的复合年增长率，这主要得益于不断扩展的数位生态系统和对现代计算基础设施的大力投资。中国、日本、印度和韩国的政府和企业正透过政策、研究机构和云端服务扩展来加强人工智慧创新。自动化、智慧製造、数位银行和医疗保健人工智慧的应用正在推动对持续训练模型的需求。该地区受益于不断增长的技能型劳动力、蓬勃发展的Start-Ups企业以及日益完善的数据可用性。智慧型手机的普及、5G的快速发展以及网路连线的改善正在加速人工智慧的普及。这些因素共同作用，使亚太地区成为人工智慧模型训练成长率最高的地区。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
  • 分析材料

第三章 市场趋势分析

  • 介绍
  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 技术分析
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球人工智慧模型训练市场（按训练类型划分）

  • 介绍
  • 监督式学习
  • 无监督学习
  • 半监督学习
  • 自主学习
  • 强化学习

6. 全球人工智慧模型训练市场以部署方式划分

  • 介绍
  • 云端基础的
  • 本地部署
  • 杂交种

7. 全球人工智慧模型训练市场（按技术划分）

  • 介绍
  • 机器学习框架
  • 深度学习架构
  • 迁移学习技术
  • 联邦学习系统

第八章 全球人工智慧模型训练市场（按应用领域划分）

  • 介绍
  • 自然语言处理（NLP）
  • 电脑视觉
  • 语音辨识
  • 预测分析
  • 自主系统
  • 财务预测

9. 全球人工智慧模型训练市场（按最终用户划分）

  • 介绍
  • 卫生保健
  • 车
  • 银行、金融服务和保险业 (BFSI)
  • 零售与电子商务
  • 製造业
  • 通讯
  • 能源与公用事业
  • 政府/国防
  • 学术与研究

第十章 全球人工智慧模型训练市场（按地区划分）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十二章：公司简介

  • Google
  • IBM
  • Amazon Web Services（AWS）
  • Microsoft
  • NVIDIA
  • Snorkel
  • Gretel
  • Shaip
  • Clickworker
  • Appen
  • Nexdata
  • Bitext
  • Aimleap
  • Deep Vision Data
  • Cogito Tech
Product Code: SMRC32237

According to Stratistics MRC, the Global AI Model Training Market is accounted for $17.15 billion in 2025 and is expected to reach $124.92 billion by 2032 growing at a CAGR of 32.8% during the forecast period. AI model training represents the developmental phase where systems study data and gradually gain decision-making intelligence. The process starts with assembling reliable datasets, cleaning them, and preparing them for input into chosen learning frameworks. Throughout training, the model tweaks internal weights to reduce mistakes and sharpen predictions. Based on goals, teams may apply supervised, unsupervised, or reinforcement approaches, supported by optimization strategies that guide learning efficiency. Performance is monitored using test samples and accuracy measures to prevent issues like overfitting. With stronger processors and larger data pools, training becomes more dynamic, enabling advanced applications and uncovering deeper insights across diverse industries.

According to Allen Institute for AI (AI2), the Semantic Scholar Open Research Corpus contains over 200 million academic papers, many of which are used to train scientific and biomedical AI models.

Market Dynamics:

Driver:

Rising adoption of big data analytics

A major growth driver for the AI Model Training Market is the swift expansion of big data analytics. Businesses produce enormous data streams from social media, IoT devices, software applications, and operational systems. To utilize this information meaningfully, enterprises are adopting training platforms capable of handling large datasets efficiently. These models support advanced predictions, automation, and personalized customer experiences. Rising data diversity encourages investment in high-performance cloud and GPU-based computing for faster training cycles. Since real-time data insights increase competitiveness, organizations depend on robust AI training to transform raw information into strategic intelligence, improving operational outcomes and enabling smarter decision-making.

Restraint:

High computational costs and infrastructure limitations

A significant challenge limiting the AI Model Training Market is the high expense of computing systems needed for large-scale learning. Complex neural networks demand premium GPUs, strong processors, and high-bandwidth cloud resources, which are costly to purchase and operate. Smaller enterprises and educational sectors face budget constraints, slowing adoption. Electricity and cooling requirements further raise operational spending, especially for continuous training. Long processing hours also delay testing and deployment of new models. As a result, some companies reduce the scope of AI projects or compromise with lightweight architectures. The overall financial burden creates hurdles for growth, particularly among organizations without advanced infrastructure.

Opportunity:

Growth of edge AI and on-device model training

Edge computing is creating a strong opportunity for the AI Model Training Market by shifting learning capabilities from centralized cloud systems to local devices. Running training processes directly on hardware limits data transfers, speeds responses, and supports greater privacy. Advancements in compact neural models, optimized processors, and federated learning make it possible to update and refine algorithms on equipment like IoT devices, robots, connected vehicles, and mobile phones. Industries benefit through real-time insights, continuous intelligence, and lower cloud dependency. This approach reduces network overload and supports reliable AI performance even where connectivity is weak, making edge-based training appealing across transportation, manufacturing, healthcare, and smart city applications.

Threat:

Rapid technological obsolescence and competitive pressure

Fast innovation in AI technologies is a significant threat to the AI Model Training Market. New hardware, architectures, and learning approaches emerge rapidly, shortening the lifespan of existing models. Companies must frequently modify or retrain systems to stay relevant, leading to higher expenses and operational complexity. Large corporations with strong resources innovate faster, putting smaller competitors at a disadvantage. Frequent technology transitions delay project cycles and create uncertainty in return on investment. Many firms struggle to choose long-term strategies when tools become outdated so quickly. As a result, the market faces competitive pressure, limited stability, and risk of reduced adoption among resource-constrained organizations.

Covid-19 Impact:

The COVID-19 pandemic influenced the AI Model Training Market in both positive and negative ways. Many companies shifted rapidly toward digital operations, which increased the need for cloud platforms, automated workflows, and intelligent analytics. This transition expanded investment in AI training, especially within online retail, telemedicine, banking, and supply chain services. At the same time, economic uncertainty and reduced technology budgets slowed adoption for smaller firms. Remote working environments encouraged the use of virtual training infrastructures and subscription-based AI development. Growing reliance on AI for medical research, remote monitoring, and safety applications also accelerated innovation. Although disruptions occurred, the pandemic ultimately boosted long-term growth and strategic importance of AI training technologies.

The cloud-based segment is expected to be the largest during the forecast period

The cloud-based segment is expected to account for the largest market share during the forecast period because it offers unmatched flexibility, speed, and scalability. Instead of purchasing costly hardware, companies rely on elastic cloud resources for data processing, storage, and high-performance GPUs. This allows teams to build, retrain, and deploy models more quickly while controlling operational costs. Cloud platforms include automated pipelines, pre-configured tools, and distributed computing features that enhance productivity and shorten project cycles. Remote working environments benefit from seamless access and collaborative development. With growing interest in deep learning, predictive analytics, and intelligent automation, cloud deployment stays dominant by delivering efficient, secure, and easily expandable AI training environments suitable for organizations of every size.

The healthcare segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare segment is predicted to witness the highest growth rate because medical organizations are rapidly integrating advanced data-driven systems. AI models are being trained for diagnostic imaging, precision medicine, drug research, and automated decision support. Hospitals and laboratories rely on powerful training infrastructures to analyze complex patient datasets and provide faster, more reliable results. Expansion of telehealth, smart medical devices, biosensors, and genetic research increases requirements for continuously improving AI algorithms. These models help identify diseases earlier and support treatment planning with improved accuracy. As digital transformation expands across the global healthcare ecosystem, demand for specialized trained medical AI tools rises at the quickest pace.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share due to its well-established AI ecosystem, strong investment in innovation, and cluster of top technology firms. It enjoys excellent computing infrastructure, generous funding resources, and a broad talent base experienced in model development and training. Industries such as healthcare, banking, and driverless vehicles located there are actively deploying and refining complex AI systems. Large cloud and AI service providers operating in the region offer seamless access to high-speed compute and massive datasets. Together, these advantages enable North America to secure the largest share of the market for training AI models across sectors.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, supported by expanding digital ecosystems and aggressive investment in modern computing infrastructure. Governments and enterprises in China, Japan, India, and South Korea are strengthening AI innovation through policies, research labs, and cloud expansion. Adoption of automation, smart manufacturing, digital banking, and healthcare AI fuels demand for continuously trained models. The region benefits from a growing skilled workforce, rapid startup activity, and increasing data availability. Higher smartphone usage, strong adoption of 5G, and improving connectivity accelerate AI deployment. These combined factors position Asia-Pacific as the region with the highest growth rate in AI model training.

Key players in the market

Some of the key players in AI Model Training Market include Google, IBM, Amazon Web Services (AWS), Microsoft, NVIDIA, Snorkel, Gretel, Shaip, Clickworker, Appen, Nexdata, Bitext, Aimleap, Deep Vision Data and Cogito Tech.

Key Developments:

In November 2025, Amazon Web Services and OpenAI announced a multi-year, strategic partnership that provides AWS's world-class infrastructure to run and scale OpenAI's core artificial intelligence (AI) workloads starting immediately. Under this new $38 billion agreement, which will have continued growth over the next seven years, OpenAI is accessing AWS compute comprising hundreds of thousands of state-of-the-art NVIDIA GPUs, with the ability to expand to tens of millions of CPUs to rapidly scale agentic workloads.

In October 2025, Google Cloud and Adobe announced an expanded strategic partnership to deliver the next generation of AI-powered creative technologies. The partnership brings together Adobe's decades of creative expertise with Google's advanced AI models-including Gemini, Veo, and Imagen-to usher in a new era of creative expression.

In September 2025, IBM and SCREEN Semiconductor Solutions Co., Ltd announced an agreement to develop cleaning processes for next-generation EUV lithography. This agreement builds on previous joint development collaboration for innovative cleaning processes that enabled the current generation of nanosheet device technology. In recent years, the adoption of EUV lithography has been accelerating to meet the growing demand for miniaturization in advanced semiconductor manufacturing processes.

Training Types Covered:

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Self-supervised Learning
  • Reinforcement Learning

Deployment Modes Covered:

  • Cloud-based
  • On-premise
  • Hybrid

Technologies Covered:

  • Machine Learning Frameworks
  • Deep Learning Architectures
  • Transfer Learning Techniques
  • Federated Learning Systems

Applications Covered:

  • Natural Language Processing (NLP)
  • Computer Vision
  • Speech Recognition
  • Predictive Analytics
  • Autonomous Systems
  • Financial Forecasting

End Users Covered:

  • Healthcare
  • Automotive
  • BFSI (Banking, Financial Services, Insurance)
  • Retail & E-commerce
  • Manufacturing
  • Telecommunications
  • Energy & Utilities
  • Government & Defense
  • Academia & Research

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI Model Training Market, By Training Type

  • 5.1 Introduction
  • 5.2 Supervised Learning
  • 5.3 Unsupervised Learning
  • 5.4 Semi-supervised Learning
  • 5.5 Self-supervised Learning
  • 5.6 Reinforcement Learning

6 Global AI Model Training Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-based
  • 6.3 On-premise
  • 6.4 Hybrid

7 Global AI Model Training Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning Frameworks
  • 7.3 Deep Learning Architectures
  • 7.4 Transfer Learning Techniques
  • 7.5 Federated Learning Systems

8 Global AI Model Training Market, By Application

  • 8.1 Introduction
  • 8.2 Natural Language Processing (NLP)
  • 8.3 Computer Vision
  • 8.4 Speech Recognition
  • 8.5 Predictive Analytics
  • 8.6 Autonomous Systems
  • 8.7 Financial Forecasting

9 Global AI Model Training Market, By End User

  • 9.1 Introduction
  • 9.2 Healthcare
  • 9.3 Automotive
  • 9.4 BFSI (Banking, Financial Services, Insurance)
  • 9.5 Retail & E-commerce
  • 9.6 Manufacturing
  • 9.7 Telecommunications
  • 9.8 Energy & Utilities
  • 9.9 Government & Defense
  • 9.10 Academia & Research

10 Global AI Model Training Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 Google
  • 12.2 IBM
  • 12.3 Amazon Web Services (AWS)
  • 12.4 Microsoft
  • 12.5 NVIDIA
  • 12.6 Snorkel
  • 12.7 Gretel
  • 12.8 Shaip
  • 12.9 Clickworker
  • 12.10 Appen
  • 12.11 Nexdata
  • 12.12 Bitext
  • 12.13 Aimleap
  • 12.14 Deep Vision Data
  • 12.15 Cogito Tech

List of Tables

  • Table 1 Global AI Model Training Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Model Training Market Outlook, By Training Type (2024-2032) ($MN)
  • Table 3 Global AI Model Training Market Outlook, By Supervised Learning (2024-2032) ($MN)
  • Table 4 Global AI Model Training Market Outlook, By Unsupervised Learning (2024-2032) ($MN)
  • Table 5 Global AI Model Training Market Outlook, By Semi-supervised Learning (2024-2032) ($MN)
  • Table 6 Global AI Model Training Market Outlook, By Self-supervised Learning (2024-2032) ($MN)
  • Table 7 Global AI Model Training Market Outlook, By Reinforcement Learning (2024-2032) ($MN)
  • Table 8 Global AI Model Training Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 9 Global AI Model Training Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 10 Global AI Model Training Market Outlook, By On-premise (2024-2032) ($MN)
  • Table 11 Global AI Model Training Market Outlook, By Hybrid (2024-2032) ($MN)
  • Table 12 Global AI Model Training Market Outlook, By Technology (2024-2032) ($MN)
  • Table 13 Global AI Model Training Market Outlook, By Machine Learning Frameworks (2024-2032) ($MN)
  • Table 14 Global AI Model Training Market Outlook, By Deep Learning Architectures (2024-2032) ($MN)
  • Table 15 Global AI Model Training Market Outlook, By Transfer Learning Techniques (2024-2032) ($MN)
  • Table 16 Global AI Model Training Market Outlook, By Federated Learning Systems (2024-2032) ($MN)
  • Table 17 Global AI Model Training Market Outlook, By Application (2024-2032) ($MN)
  • Table 18 Global AI Model Training Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 19 Global AI Model Training Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 20 Global AI Model Training Market Outlook, By Speech Recognition (2024-2032) ($MN)
  • Table 21 Global AI Model Training Market Outlook, By Predictive Analytics (2024-2032) ($MN)
  • Table 22 Global AI Model Training Market Outlook, By Autonomous Systems (2024-2032) ($MN)
  • Table 23 Global AI Model Training Market Outlook, By Financial Forecasting (2024-2032) ($MN)
  • Table 24 Global AI Model Training Market Outlook, By End User (2024-2032) ($MN)
  • Table 25 Global AI Model Training Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 26 Global AI Model Training Market Outlook, By Automotive (2024-2032) ($MN)
  • Table 27 Global AI Model Training Market Outlook, By BFSI (Banking, Financial Services, Insurance) (2024-2032) ($MN)
  • Table 28 Global AI Model Training Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 29 Global AI Model Training Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 30 Global AI Model Training Market Outlook, By Telecommunications (2024-2032) ($MN)
  • Table 31 Global AI Model Training Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 32 Global AI Model Training Market Outlook, By Government & Defense (2024-2032) ($MN)
  • Table 33 Global AI Model Training Market Outlook, By Academia & Research (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.