AI训练资料集市场：现况分析与预测（2024-2032）

由于人工智慧技术在各行业应用的日益普及，人工智慧训练资料集市场预计将以约21.5%的复合年增长率强劲成长。近年来，人工智慧 (AI) 经历了前所未有的成长和进步，人工智慧驱动的应用和技术在各个行业中越来越受欢迎。人工智慧的快速扩张促使对高品质、多样化和全面的人工智慧训练资料集的需求激增，以支援这些先进系统。此外，医疗保健、金融、电子商务和交通等领域也越来越多地采用人工智慧技术，也是推动人工智慧训练资料集需求的主要因素。随着企业和组织寻求利用人工智慧的力量来增强营运、改进决策并提供个人化体验，需要强大、可靠且多样化的资料来训练这些人工智慧模型，对资料集的需求正在迅速增加。此外，机器学习（ML）和深度学习（DL）演算法的日益普及和扩散是促使人工智慧训练资料集需求激增的主要因素。这些先进技术依赖大量资料来训练模型、学习模式并做出准确的预测。例如，在韩国，到 2022 年，客户资料将成为训练人工智慧 (AI) 模型的主要来源，约 70% 的受访公司表示如此。此外，约 62% 的受访者表示他们使用内部资料来训练人工智慧模型。

依类型划分，市场分为文字、音讯、图像、视讯和其他（感测器和地理）。文字资料集是目前用于训练各种人工智慧和机器学习模型最广泛使用的资料集。由于互联网、书籍、文章、社交媒体和其他各种来源提供了大量信息，文本数据在数位时代无处不在。文字资料集通常比其他资料类型（如音讯和视讯）更容易收集、储存和处理。此外，文字资料可用于训练各种人工智慧和机器学习模型，包括用于情绪分析、文字分类、语言生成和机器翻译等任务的自然语言处理 (NLP) 模型。文字资料还可用于训练 NLP 以外任务的模型，例如文件摘要、资讯检索，甚至某些类型的图像和影片分析任务。文字资料的多功能性使得能够开发各种人工智慧和机器学习应用程序，从聊天机器人和虚拟助理到内容推荐系统和自动写作工具。此外，与需要更强大的硬体和更大的计算资源（例如高解析度图像和视讯）的其他资料类型相比，文字资料通常需要更少的计算工作量来处理。这使得基于文字的人工智慧和机器学习模型更易于开发和部署，尤其是在资源受限的设备和运算能力有限的场景中。这些因素正在推动环境的发展，并增加了对用于训练各种人工智慧和机器学习模型的文字资料集的需求。

根据部署模式，市场分为云端和本地。基于云端的部署已成为训练 AI 和 ML 模型最广泛使用的方法，大多数组织都选择这种方法。其主要原因是基于云端的操作所带来的灵活性和可扩展性。基于云端的部署提供了无与伦比的可扩展性，使企业能够根据需求的变化轻鬆增加或减少运算资源。这对于训练复杂的人工智慧和机器学习模型尤其重要，因为这些模型通常需要大量的运算和储存容量。此外，云端服务供应商通常会大力投资最新的硬体和软体技术，使企业能够获得先进的运算资源，例如强大的 GPU 和机器学习专用硬体。这使得公司能够利用尖端技术，而无需进行大量的内部投资。此外，基于云端的部署促进了远端资料存取和协作，使分散式团队能够在人工智慧和机器学习专案上无缝协作。这对于团队分布在不同地点的组织或需要与外部合作伙伴和资料来源协作的组织尤其有利。这些发展和其他发展极大地促进了基于云端的模型在各种人工智慧和机器学习任务训练中的广泛采用。

根据最终用户行业，市场分为 IT/电信、零售/消费品、医疗保健、汽车、BFSI 和其他（政府/製造）。BFSI 产业在人工智慧采用方面处于领先地位。例如，根据教育科技Edtech公司 Great Learning 于 2023 年 9 月发布的报告，印度的银行、金融服务和保险 (BFSI) 行业占数据科学和分析职位的三分之一以上。这一显着增长归因于人工智慧、机器学习和大数据分析等新兴技术的日益使用。这些进步正在推动风险管理、诈欺侦测和客户服务等领域的进步。该行业对人工智慧的快速接受是因为它是数据驱动的。BFSI 产业本质上是数据驱动的，处理大量的金融交易、客户资讯和市场数据。事实证明，这些丰富的资料是有效训练和部署人工智慧和机器学习 (ML) 模型的关键要素。此外，BFSI 领域的人工智慧解决方案已证明其能够简化从诈欺侦测和风险管理到个人化客户服务和投资组合最佳化等流程。这显着提高了营运效率并降低了成本。此外，在竞争激烈的 BFSI 环境中，提供无缝且个人化的客户体验已成为策略当务之急。人工智慧驱动的聊天机器人、对话式介面和预测分析使银行和金融机构能够更有效地预测和回应客户需求。这些因素对 BFSI 领域采用人工智慧领域做出了重大贡献。

为了更瞭解TLS 的市场采用情况，我们将市场分为北美（美国、加拿大、北美其他地区）、欧洲（德国、英国、法国、西班牙、义大利、欧洲其他地区）、亚太地区（中国、日本、印度） 、澳洲）、其他亚太地区）以及世界其他地区。北美已成为人工智慧训练资料集最大、成长最快的市场之一。美国拥有史丹佛大学、麻省理工学院和卡内基美隆大学等一些世界领先的研究型大学，并且在人工智慧和机器学习研究方面取得了重大进展。此外，Google、Microsoft、Amazon等知名科技公司在北美建立了最先进的人工智慧实验室，进一步促进了该领域的创新和进步。此外，美国政府认识到人工智慧的战略重要性，并透过国家人工智慧计画等措施大力投资支持研究和开发。此外，北美科技巨头正在积极投资开发和留住顶尖人工智慧和机器学习人才，创造一个自我强化的创新和成长循环。最后，北美，尤其是美国，拥有蓬勃发展的创投生态系统，已向人工智慧和机器学习新创公司和公司注入了数十亿美元。硅谷、波士顿和纽约等主要科技中心的存在正在推动投资资本进入人工智慧/机器学习产业。例如，根据S&P Global Market Intelligence的数据，2023年对生成式人工智慧公司的投资将大幅增加，超过整体併购活动的下降幅度。私募股权公司在生成式人工智慧领域投资了 21.8 亿美元，是去年投资总额的两倍。资本激增之际，2023 年私募股权支持的併购交易在全产业范围内下降。这些因素使北美成为人工智慧和机器学习产业的主导力量，促使对人工智慧训练资料集服务的需求增加，以支援人工智慧产业前所未有的成长速度。

市场上营运的主要公司包括Google、Microsoft、 Amazon Web Services, Inc.、IBM、Oracle、Alegion AI, Inc.、TELUS International、Lionbridge Technologies、LLC、Samasource Impact Sourcing, Inc.、Appen Limited等。

目录

第1章 市场介绍

• 市场定义
• 主要目标
• 利益相关者
• 限制

第2章 研究方法或前提条件

• 调查过程
• 调查方法
• 受访者简介

第3章 执行摘要

• 业界总结
• 分部展望
• 市场成长的力量
• 区域展望

第4章 市场动态

• 促进因素
• 机会
• 抑制因素
• 趋势
• PESTEL 分析
• 需求面分析
• 供给侧分析
  • 合併/收购
  • 投资场景
  • 产业洞察：主要新创公司及其独特策略

第5章 价格分析

• 区域价格分析
• 影响价格的因素

第6章 2022-2032 年全球人工智慧训练资料集市场收入

第7章 市场洞察：依类型

• 文本
• 声音的
• 图像
• 视频
• 其他（感测器/地理）

第8章 市场洞察：依部署模式

• 云
• 本地

第9章 市场洞察：依最终用户分类

• IT/通讯
• 零售/消费品
• 卫生保健
• 汽车
• 银行、金融服务和保险 (BFSI)
• 其他（政府/製造业）

第10章 市场洞察：依地区

• 北美
  • 美国
  • 加拿大
  • 其他北美
• 欧洲
  • 德国
  • 英国
  • 法国
  • 义大利
  • 西班牙
  • 欧洲其他地区
• 亚太地区
  • 中国
  • 日本
  • 印度
  • 澳大利亚
  • 其他亚太地区
• 世界其他地区

第11章 价值链分析

• 边际分析
• 进入市场的企业名单

第12章 竞争格局

• 比赛仪表板
• 竞争市场定位分析
• 波特五力分析

第13章 公司简介

• Google
  • Company Overview
  • Key Financials
  • SWOT Analysis
  • Product Portfolio
  • Recent Developments
• Microsoft
• Amazon Web Services, Inc.
• IBM
• Oracle
• Alegion AI, Inc
• TELUS International
• Lionbridge Technologies, LLC
• Samasource Impact Sourcing, Inc.
• Appen Limited

第14章 缩写与先决条件

第15章 附录

AI training datasets are the foundational data used to train and develop machine learning and artificial intelligence models. These datasets consist of labeled examples that the AI models use to learn patterns and relationships and make accurate predictions. Datasets are collected from various sources such as databases, websites, articles, video transcripts, social media, and other relevant data sources. The goal is to gather a diverse and representative set of data. The raw data is carefully labeled and annotated to provide the AI model with accurate information from which to learn. This involves categorizing, tagging, and describing the data.

The AI Training Dataset Market is expected to grow at a strong CAGR of around 21.5%, owing to the growing proliferation of AI technology applications across various industries. Artificial Intelligence (AI) has witnessed unprecedented growth and advancements in recent years, with AI-powered applications and technologies becoming increasingly prevalent across various industries. This rapid expansion of AI has led to a corresponding surge in the demand for high-quality, diverse, and comprehensive AI training datasets to power these advanced systems. Furthermore, the growing adoption of AI-powered technologies across sectors such as healthcare, finance, e-commerce, and transportation has been a major driver of the demand for AI training datasets. As companies and organizations seek to leverage the power of AI to enhance their operations, improve decision-making, and deliver personalized experiences, the need for robust, reliable, and diverse datasets to train these AI models has skyrocketed. Additionally, the growing popularity and widespread adoption of machine learning (ML) and deep learning (DL) algorithms have been a significant factor in the surge of demand for AI training datasets. These advanced techniques rely on vast amounts of data to train their models, learn patterns, and make accurate predictions. For instance, in South Korea, customer data emerged as the primary information source for training artificial intelligence (AI) models in 2022, as stated by almost 70 percent of the surveyed companies. Furthermore, approximately 62 percent of the respondents indicated their utilization of internal data for training their AI models.

Based on type, the market is segmented into text, audio, image, video, and others (sensor and geo). Text datasets are the most widely used datasets for training various AI and ML models currently. Text data is ubiquitous in the digital age, with vast amounts of information available on the internet, in books, articles, social media, and various other sources. Text datasets are generally easier to collect, store, and process compared to other data types, such as audio or video. Furthermore, Text data can be used to train a wide range of AI and ML models, including natural language processing (NLP) models for tasks like sentiment analysis, text classification, language generation, and machine translation. Text data can also be used to train models for tasks beyond NLP, such as document summarization, information retrieval, and even some types of image and video analysis tasks. The versatility of text data allows for the development of a diverse range of AI and ML applications, from chatbots and virtual assistants to content recommendation systems and automated writing tools. Additionally, text data is generally less computationally intensive to process compared to other data types, such as high-resolution images or video, which require more powerful hardware and greater computational resources. This makes text-based AI and ML models more accessible and feasible to develop and deploy, especially on resource-constrained devices or in scenarios with limited computational power. Factors such as these are fostering a conducive environment, driving the surge in demand for text datasets for the training of various AI and ML models.

Based on deployment mode, the market is bifurcated into cloud and on-premise. Cloud-based deployment has emerged as the most widely used avenue for training AI and ML models, with a majority of organizations opting for this approach. Primarily driven by the flexibility and scalability that comes with cloud-based operation. Cloud-based deployment offers unparalleled scalability, allowing organizations to easily scale up or down their computing resources as per their changing needs. This is particularly crucial for training complex AI and ML models, which often require significant computational power and storage capacity. Furthermore, cloud service providers often invest heavily in the latest hardware and software technologies, ensuring that organizations have access to state-of-the-art computing resources, including powerful GPUs and specialized machine learning hardware. This allows organizations to leverage cutting-edge technologies without the need for significant in-house investments. Additionally, cloud-based deployment facilitates remote data access and collaboration, enabling distributed teams to work together on AI and ML projects seamlessly. This is particularly beneficial for organizations with geographically dispersed teams or those that need to collaborate with external partners or data sources. These developments, among others, have contributed substantially to the widespread adoption of cloud-based models for training various AI and ML operations.

Based on the end-user industry, the market is segmented into IT and telecommunication, retail and consumer goods, healthcare, automotive, BFSI, and others (government and manufacturing). The BFSI sector stands out as the frontrunner in AI adoption. For instance, according to the report released by Edtech company Great Learning in September 2023, the banking, financial services, and insurance (BFSI) sector in India accounted for more than one-third of data science and analytics jobs. This significant growth can be attributed to the increasing utilization of emerging technologies such as artificial intelligence, machine learning, and big data analytics. These advancements have particularly driven progress in areas like risk management, fraud detection, and customer service. This sector's rapid embrace of AI can be attributed to the industry's data-driven nature. The BFSI industry is inherently data-driven, dealing with vast amounts of financial transactions, customer information, and market data. This abundance of data has proven to be a crucial enabler for the effective training and deployment of AI and machine learning (ML) models. Furthermore, AI-powered solutions in the BFSI sector have demonstrated their ability to streamline various processes, from fraud detection and risk management to personalized customer service and investment portfolio optimization. This has led to significant improvements in operational efficiency and cost savings. Additionally, in the highly competitive BFSI landscape, delivering a seamless and personalized customer experience has become a strategic imperative. AI-driven chatbots, conversational interfaces, and predictive analytics have enabled banks and financial institutions to anticipate and cater to customer needs more effectively. Factors such as these have contributed significantly to the global adoption of AI within the BFSI sector.

For a better understanding of the market adoption of TLS, the market is analyzed based on its worldwide presence in countries such as North America (The U.S., Canada, and the Rest of North America), Europe (Germany, The U.K., France, Spain, Italy, Rest of Europe), Asia-Pacific (China, Japan, India, Australia, Rest of Asia-Pacific), Rest of World. North America has emerged as one of the largest and fastest-growing markets for AI training datasets. The United States is home to some of the world's leading research universities, such as Stanford, MIT, and Carnegie Mellon, which have made significant strides in AI and ML research. Furthermore, prominent tech companies, including Google, Microsoft, and Amazon, have established cutting-edge AI research labs in North America, further driving innovation and advancements in the field. Additionally, the U.S. government has recognized the strategic importance of AI and has invested heavily in supporting research and development through initiatives like the National Artificial Intelligence Initiative. Moreover, major tech companies in North America have been actively investing in training and retaining top AI and ML talent, creating a self-reinforcing cycle of innovation and growth. Lastly, North America, especially the U.S., is home to a thriving venture capital ecosystem that has been pouring billions of dollars into AI and ML startups and companies. The presence of major tech hubs, such as Silicon Valley, Boston, and New York, has facilitated the flow of investment capital into the AI and ML industry. For instance, in 2023, according to the S&P Global Market Intelligence data, investments in generative AI companies saw a significant increase, surpassing the decline in overall M&A activity. Private equity firms invested USD 2.18 billion in generative AI, doubling the previous year's total. This surge in capital occurred amidst a decrease in private equity-backed M&A transactions across industries in 2023. Factors such as these have made North America a predominant force in the AI and ML industry, consequently boosting the demand for AI training dataset services to support this unprecedented growth rate of the AI industry.

Some of the major players operating in the market include Google, Microsoft; Amazon Web Services, Inc.; IBM; Oracle; Alegion AI, Inc.; TELUS International; Lionbridge Technologies, LLC; Samasource Impact Sourcing, Inc.; and Appen Limited.

TABLE OF CONTENTS

1.MARKET INTRODUCTION

• 1.1. Market Definitions
• 1.2. Main Objective
• 1.3. Stakeholders
• 1.4. Limitation

2.RESEARCH METHODOLOGY OR ASSUMPTION

• 2.1. Research Process of the AI Training Dataset Market
• 2.2. Research Methodology of the AI Training Dataset Market
• 2.3. Respondent Profile

3.EXECUTIVE SUMMARY

• 3.1. Industry Synopsis
• 3.2. Segmental Outlook
• 3.3. Market Growth Intensity
• 3.4. Regional Outlook

4.MARKET DYNAMICS

• 4.1. Drivers
• 4.2. Opportunity
• 4.3. Restraints
• 4.4. Trends
• 4.5. PESTEL Analysis
• 4.6. Demand Side Analysis
• 4.7. Supply Side Analysis
  • 4.7.1. Merger & Acquisition
  • 4.7.2. Investment Scenario
  • 4.7.3. Industry Insights: Leading Startups and Their Unique Strategies

5.PRICING ANALYSIS

• 5.1. Regional Pricing Analysis
• 5.2. Price Influencing Factors

6.GLOBAL AI TRAINING DATASET MARKET REVENUE (USD BN), 2022-2032F

7.MARKET INSIGHTS BY TYPE

• 7.1. Text
• 7.2. Audio
• 7.3. Image
• 7.4. Video
• 7.5. Other (Sensor and Geo)

8.MARKET INSIGHTS BY DEPLOYMENT MODE

• 8.1. Cloud
• 8.2. On-Premises

9.MARKET INSIGHTS BY END-USER

• 9.1. IT and Telecommunication
• 9.2. Retail and Consumer Goods
• 9.3. Healthcare
• 9.4. Automotive
• 9.5. Banking, Financial Services, and Insurance (BFSI)
• 9.6. Others (Government and Manufacturing)

10.MARKET INSIGHTS BY REGION

• 10.1. North America
  • 10.1.1. U.S.
  • 10.1.2. Canada
  • 10.1.3. Rest of North America
• 10.2. Europe
  • 10.2.1. Germany
  • 10.2.2. U.K.
  • 10.2.3. France
  • 10.2.4. Italy
  • 10.2.5. Spain
  • 10.2.6. Rest of Europe
• 10.3. Asia-Pacific
  • 10.3.1. China
  • 10.3.2. Japan
  • 10.3.3. India
  • 10.3.4. Australia
  • 10.3.5. Rest of Asia-Pacific
• 10.4. Rest of World

11.VALUE CHAIN ANALYSIS

• 11.1. Marginal Analysis
• 11.2. List of Market Participants

12.COMPETITIVE LANDSCAPE

• 12.1. Competition Dashboard
• 12.2. Competitor Market Positioning Analysis
• 12.3. Porter Five Force Analysis

13.COMPANY PROFILED

• 13.1. Google
  • 13.1.1. Company Overview
  • 13.1.2. Key Financials
  • 13.1.3. SWOT Analysis
  • 13.1.4. Product Portfolio
  • 13.1.5. Recent Developments
• 13.2. Microsoft
• 13.3. Amazon Web Services, Inc.
• 13.4. IBM
• 13.5. Oracle
• 13.6. Alegion AI, Inc
• 13.7. TELUS International
• 13.8. Lionbridge Technologies, LLC
• 13.9. Samasource Impact Sourcing, Inc.
• 13.10. Appen Limited

14.ACRONYMS & ASSUMPTION

15.ANNEXURE