Summary

The market for AI training datasets is expected to increase from USD 2.82 billion in 2024 to USD 9.58 billion in 2029, experiencing a compound annual growth rate (CAGR) of 27.7% from 2024 to 2029. The demand for AI training datasets is rapidly increasing as various sectors look for more machine learning and AI uses. A key factor driving the growth of the market is the increasing demand for top-notch, varied data collections to properly train AI models, especially in industries such as healthcare, finance, and autonomous vehicles. However, concerns regarding data privacy and compliance with regulations continue to pose a major barrier that could hinder data collection and restrict access to personal data. Businesses encounter difficulties in obtaining and controlling data that comply with performance and regulation requirements, while also harmonizing innovation and ethical factors.
“By offering, dataset creation segment is expected to register the fastest market growth rate during the forecast period.”
The dataset creation segment is expected to have the quickest increase in the market in the forecast period, due to the growing need for top-notch data in different industries. Businesses are realizing the significance of making decisions based on data and are therefore making substantial investments in developing thorough and precise sets of data. This part takes advantage of AI and ML progress, which simplify data collection and processing, enabling businesses to create datasets more quickly and on a larger scale. Additionally, the rapid growth of this sector is fueled by the increasing number of IoT devices, and the growing amount of data produced from digital interactions. Companies are prioritizing the creation of large data sets to conduct predictive analysis, comprehend customer actions, and devise tailored marketing tactics to improve their results. Rules like GDPR and CCPA have prompted businesses to focus on ethical ways of collecting data, creating a demand for customized datasets that abide by the regulations. Companies require tailored data sets to meet specific business requirements in order to stay competitive in their respective industries and experience market growth.
“By dataset selling, Off-the-Shelf (OTS) datasets segment is expected to have the largest market share during the forecast period.”
The OTS datasets are expected to lead the dataset selling segment in market because of their inexpensive price, easy access, and immediate suitability for various uses. Companies are opting for pre-made datasets more often as they save time on data collection and preparation, enabling a swift adoption of data-driven strategies. The rising demand for data analysis in different sectors such as healthcare, finance, and marketing are pushing this trend further, as companies seek to leverage existing data for improved decision-making and obtaining valuable insights. In addition, the rise of artificial intelligence and machine learning technologies has raised the demand for top-notch data to train models, resulting in a heavier reliance on pre-made datasets. The use of ready-made datasets is expected to rise steadily in the upcoming years as businesses prioritize adaptability and remaining competitive.
“By annotation type, synthetic datasets segment is expected to register the fastest market growth rate during the forecast period.”
Throughout the predicted period, the synthetic datasets segment in the AI training dataset market is expected to experience the most significant increase in growth rate. Synthetic datasets generate abundant data simulating real-world scenarios, solving problems of insufficient data and privacy issues associated with authentic datasets. Customizing synthetic data to suit particular purposes increases its attractiveness, since it can be tailored to fulfill the diverse demands of artificial intelligence models across different industries. Progress in developing models and simulation techniques enhances the accuracy and authenticity of synthetic data, ultimately boosting its efficacy in training machine learning algorithms. The demand for robust and flexible datasets is projected to increase as companies focus on improving their AI capabilities, underscoring the importance of synthetic datasets in future AI projects. This phenomenon is encouraging ethical AI methods by employing artificial data to reduce prejudice and ensure fairer outcomes in AI uses.
“By Region, North America to have the largest market share in 2024, and Asia Pacific is slated to grow at the fastest rate during the forecast period.”
In 2024, North America is expected to dominate the AI training dataset market with the largest market share. The reason for this dominance is the existence of big tech firms, significant investments in AI, and a strong network of data-centric advancements. Companies in North America are increasingly integrating artificial intelligence to enhance their operations, leading to a demand for high-quality training data. In the meantime, it is expected that the Asia Pacific region will show the highest rate of growth in the predicted period. The rapid expansion is due to additional investments in AI, higher internet usage, and a growing number of AI and machine learning startups. China and India are leading the way in embracing AI technologies, thanks to their abundant data and young population well-versed in technology.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI training dataset market.
 By Company: Tier I – 18%, Tier II – 52%, and Tier III – 30%
 By Designation: C-Level Executives – 42%, D-Level Executives – 36%, and others – 22%
 By Region: North America – 42%, Europe – 26%, Asia Pacific – 21%, Middle East & Africa – 4%, and Latin America – 7%
The report includes the study of key players offering AI training dataset solutions. It profiles major vendors in the AI training dataset market. The major players in the AI training dataset market include Google (US), IBM (US), AWS (US), Microsoft (US), NVIDIA (US), Snorkel (US), Gretel (US), Shaip (US), Clickworker (US), Appen (Australia), Nexdata (US), Bitext (US), Aimleap (US), Deep Vision Data (US), Cogito Tech (US), Sama (US), Scale AI (US), Lionbridge Technologies (US), Alegion (US), TELUS International (Canada), iMerit (US), Labelbox (US), V7Labs (UK), Defined.ai (US), SuperAnnotate (US), LXT (Canada), Toloka AI (Netherlands), Innodata (US), Kili technology (France), HumanSignal (US), Superb AI (US), Hugging Face (US), CloudFactory (UK), FileMarket (Hong Kong), TagX (UAE), Roboflow (US), Supervise.ly (Estonia), Encord (UK), TransPerfect (US), Keylabs (Israel), and Data.world (US).
Research coverage
This research report categorizes the AI training dataset Market by Offering (Dataset Creation and Dataset Selling), by Dataset Creation (Dataset Creation Software, and Dataset Creation Services), by Dataset Selling (Off-The-Shelf (OTS) Datasets, and Dataset Marketplaces), by Annotation Type (Pre-Labeled Datasets, Unlabeled Datasets, and Synthetic Datasets), by Data Modality (Text, Image, Audio & Speech, Video and Multimodal), By Type (Generative AI and Other AI), by End User (BFSI, Software & Technology Providers, Telecommunications, Automotive, Media & Entertainment, Government & Defense, Healthcare & Life Sciences, Manufacturing, Retail & Consumer Goods, And Other End Users) and by Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI training dataset market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services; key strategies; contracts, partnerships, agreements, new product & service launches, mergers and acquisitions, and recent developments associated with the AI training dataset market. Competitive analysis of upcoming startups in the AI training dataset market ecosystem is covered in this report.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI training dataset market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:
• Analysis of key drivers (increasing demand for diverse and continuously updated multimodal datasets for generative AI models, rising demand for multilingual datasets for conversational AI, demand for high-quality labeled data for autonomous vehicles, and Increased used of synthetic data for rare event simulation), restraints (legal risks of web-scraped data due to copyright infringement and limited access to high-quality medical datasets due to HIPAA compliance), opportunities (growing demand for specialized data annotation services in diverse fields, synthetic data generation and privacy-preserving techniques for augmented training data, and creation of customized AI Datasets and specialized formats (3D, AR/VR) for Enterprise Solutions), and challenges (data quality and relevance issues like inconsistency, bias, keeping datasets up to date, and diverse dataset formats and inconsistent annotation practices may hinder integration and reliability).
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI training dataset market.
• Market Development: Comprehensive information about lucrative markets – the report analyses the AI training dataset market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI training dataset market.
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players like Google (US), IBM (US), AWS (US), Microsoft (US), NVIDIA (US), Snorkel (US), Gretel (US), Shaip (US), Clickworker (US), Appen (Australia), Nexdata (US), Bitext (US), Aimleap (US), Deep Vision Data (US), Cogito Tech (US), Sama (US), Scale AI (US), Lionbridge Technologies (US), Alegion (US), TELUS International (Canada), iMerit (US), Labelbox (US), V7Labs (UK), Defined.ai (US), SuperAnnotate (US), LXT (Canada), Toloka AI (Netherlands), Innodata (US), Kili technology (France), HumanSignal (US), Superb AI (US), Hugging Face (US), CloudFactory (UK), FileMarket (Hong Kong), TagX (UAE), Roboflow (US), Supervise.ly (Estonia), Encord (UK), TransPerfect (US), Keylabs (Israel), and Data.world (US) among others in the AI training dataset market. The report also helps stakeholders understand the pulse of the AI training dataset market and provides them with information on key market drivers, restraints, challenges, and opportunities.

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Table of Contents

1 INTRODUCTION 43
1.1 STUDY OBJECTIVES 43
1.2 MARKET DEFINITION 43
1.2.1 INCLUSIONS AND EXCLUSIONS 44
1.3 MARKET SCOPE 45
1.3.1 MARKET SEGMENTATION 45
1.3.2 YEARS CONSIDERED 48
1.4 CURRENCY CONSIDERED 49
1.5 STAKEHOLDERS 49
2 RESEARCH METHODOLOGY 50
2.1 RESEARCH DATA 50
2.1.1 SECONDARY DATA 51
2.1.2 PRIMARY DATA 51
2.1.2.1 Breakup of primary profiles 52
2.1.2.2 Key industry insights 52
2.2 MARKET BREAKUP AND DATA TRIANGULATION 53
2.3 MARKET SIZE ESTIMATION 54
2.3.1 TOP-DOWN APPROACH 54
2.3.2 BOTTOM-UP APPROACH 55
2.4 MARKET FORECAST 59
2.5 RESEARCH ASSUMPTIONS 60
2.6 RESEARCH LIMITATIONS 62
3 EXECUTIVE SUMMARY 63
4 PREMIUM INSIGHTS 71
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI TRAINING DATASET MARKET 71
4.2 AI TRAINING DATASET MARKET, BY TOP THREE DATA MODALITIES 72
4.3 NORTH AMERICA: AI TRAINING DATASET MARKET,
BY ANNOTATION TYPE AND END USER 72
4.4 AI TRAINING DATASET MARKET, BY REGION 73
 
5 MARKET OVERVIEW AND INDUSTRY TRENDS 74
5.1 INTRODUCTION 74
5.2 MARKET DYNAMICS 74
5.2.1 DRIVERS 75
5.2.1.1 Increasing need for diverse and continuously updated multimodal datasets for generative AI models 75
5.2.1.2 Rising use of multilingual datasets in conversational AI 75
5.2.1.3 Growing demand for high-quality labeled data for autonomous vehicles 76
5.2.1.4 Rising adoption of synthetic data for rare event simulation 76
5.2.2 RESTRAINTS 77
5.2.2.1 Legal risks of web-scraped data due to copyright infringement 77
5.2.2.2 Limited access to high-quality medical datasets due to HIPAA compliance 77
5.2.3 OPPORTUNITIES 78
5.2.3.1 Growing demand for specialized data annotation services in diverse fields 78
5.2.3.2 Synthetic data generation and privacy-preserving techniques for augmented training data 78
5.2.3.3 Creation of customized AI datasets and specialized formats for enterprise solutions 79
5.2.4 CHALLENGES 79
5.2.4.1 Data quality and relevance issues 79
5.2.4.2 Diverse dataset formats and inconsistent annotation practices 79
5.3 EVOLUTION OF AI TRAINING DATASET 80
5.4 SUPPLY CHAIN ANALYSIS 82
5.5 ECOSYSTEM ANALYSIS 84
5.5.1 DATA COLLECTION SOFTWARE PROVIDERS 86
5.5.2 DATA LABELING AND ANNOTATION PLATFORM PROVIDERS 87
5.5.3 SYNTHETIC DATA PROVIDERS 87
5.5.4 DATA AUGMENTATION TOOL PROVIDERS 87
5.5.5 OFF-THE-SHELF (OTS) DATASET PROVIDERS 87
5.5.6 AI TRAINING DATASET SERVICE PROVIDERS 88
5.6 INVESTMENT AND FUNDING SCENARIO 88
5.7 IMPACT OF GENERATIVE AI ON AI TRAINING DATASET MARKET 91
5.7.1 DATA AUGMENTATION FOR IMAGE RECOGNITION 92
5.7.2 SYNTHETIC TEXT GENERATION FOR NLP 92
5.7.3 SPEECH AND AUDIO DATA SYNTHESIS 92
5.7.4 SIMULATED USER INTERACTION DATA 92
5.7.5 BIAS MITIGATION IN DATASETS 92
5.7.6 SCENARIO TESTING FOR PREDICTIVE MODELS 92
 
5.8 CASE STUDY ANALYSIS 93
5.8.1 CASE STUDY 1: CLICKWORKER BOOSTS AI TRAINING DATASET FOR AUTOMOTIVE SYSTEMS, IMPROVING SPEECH RECOGNITION ACCURACY 93
5.8.2 CASE STUDY 2: APPEN ENHANCES MICROSOFT TRANSLATOR WITH COMPREHENSIVE AI TRAINING DATASETS FOR 110 LANGUAGES 93
5.8.3 CASE STUDY 3: COGITO TECH LLC ENHANCES CARDIAC SURGERY WITH AI-DRIVEN AORTIC VALVE DATASETS 94
5.8.4 CASE STUDY 4: ENHANCING AI TRAINING DATASETS FOR PAIN REDUCTION THROUGH HINGE HEALTH'S SUCCESS WITH SUPERANNOTATE 94
5.8.5 CASE STUDY 5: OUTREACH ENHANCES AI TRAINING WITH LABEL STUDIO 95
5.8.6 CASE STUDY 6: ENCORD ADDRESSES KEY CHALLENGES IN SURGICAL VIDEO ANNOTATION FOR ENHANCED DATA QUALITY AND EFFICIENCY 96
5.9 TECHNOLOGY ANALYSIS 96
5.9.1 KEY TECHNOLOGIES 97
5.9.1.1 Data labeling and annotation 97
5.9.1.2 Synthetic data generation 97
5.9.1.3 Data augmentation 97
5.9.1.4 Human-in-the-loop (HITL) feedback systems 98
5.9.1.5 Active learning 98
5.9.1.6 Data cleansing and preprocessing 98
5.9.1.7 Bias detection and mitigation 99
5.9.1.8 Dataset versioning and management 99
5.9.2 COMPLEMENTARY TECHNOLOGIES 99
5.9.2.1 Cloud storage and data lakes 99
5.9.2.2 MLOps and model management 100
5.9.2.3 Data governance 100
5.9.2.4 Machine learning frameworks 100
5.9.3 ADJACENT TECHNOLOGIES 101
5.9.3.1 Federated learning 101
5.9.3.2 Edge AI for data processing 101
5.9.3.3 Differential privacy 101
5.9.3.4 AutoML 102
5.9.3.5 Transfer learning 102
5.10 REGULATORY LANDSCAPE 102
5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 103
5.10.2 REGULATIONS: AI TRAINING DATASET 107
5.10.2.1 North America 107
5.10.2.1.1 Blueprint for an AI Bill of Rights (US) 107
5.10.2.1.2 Directive on Automated Decision-Making (Canada) 107
5.10.2.2 Europe 108
5.10.2.2.1 UK AI Regulation White Paper 108
5.10.2.2.2 Gesetz zur Regulierung Künstlicher Intelligenz (AI Regulation Law - Germany) 108
5.10.2.2.3 Loi pour une République numérique (Digital Republic Act - France) 108
5.10.2.2.4 Codice in materia di protezione dei dati personali (Data Protection Code - Italy) 109
5.10.2.2.5 Ley de Servicios Digitales (Digital Services Act - Spain) 109
5.10.2.2.6 Dutch Data Protection Authority (Autoriteit Persoonsgegevens) Guidelines 109
5.10.2.2.7 The Swedish National Board of Trade AI Guidelines 110
5.10.2.2.8 Danish Data Protection Agency (Datatilsynet) AI Recommendations 110
5.10.2.2.9 Artificial Intelligence 4.0 (AI 4.0) Program - Finland 110
5.10.2.3 Asia Pacific 111
5.10.2.3.1 Personal Data Protection Bill (PDPB) & National Strategy on AI (NSAI) - India 111
5.10.2.3.2 The Basic Act on the Advancement of Utilizing Public and Private Sector Data & AI Guidelines - Japan 111
5.10.2.3.3 New Generation Artificial Intelligence Development Plan & AI Ethics Guidelines - China 111
5.10.2.3.4 Framework Act on Intelligent Informatization – South Korea 112
5.10.2.3.5 AI Ethics Framework (Australia) & AI Strategy (New Zealand) 112
5.10.2.3.6 Model AI Governance Framework - Singapore 113
5.10.2.3.7 National AI Framework - Malaysia 113
5.10.2.3.8 National AI Roadmap - Philippines 113
5.10.2.4 Middle East & Africa 114
5.10.2.4.1 Saudi Data & Artificial Intelligence Authority (SDAIA) Regulations 114
5.10.2.4.2 UAE National AI Strategy 2031 114
5.10.2.4.3 Qatar National AI Strategy 114
5.10.2.4.4 National Artificial Intelligence Strategy (2021-2025)- Turkey 115
5.10.2.4.5 African Union (AU) AI Framework 115
5.10.2.4.6 Egyptian Artificial Intelligence Strategy 115
5.10.2.4.7 Kuwait National Development Plan (New Kuwait Vision 2035) 116
5.10.2.5 Latin America 116
5.10.2.5.1 Brazilian General Data Protection Law (LGPD) 116
5.10.2.5.2 Federal Law on the Protection of Personal Data Held by Private Parties - Mexico 116
5.10.2.5.3 Argentina Personal Data Protection Law (PDPL) & AI Ethics Framework 117
5.10.2.5.4 Chilean Data Protection Law & National AI Policy 117
5.10.2.5.5 Colombian Data Protection Law (Law 1581) & AI Ethics Guidelines 117
5.10.2.5.6 Peruvian Personal Data Protection Law & National AI Strategy 118
 
5.11 PATENT ANALYSIS 118
5.11.1 METHODOLOGY 118
5.11.2 PATENTS FILED, BY DOCUMENT TYPE 118
5.11.3 INNOVATION AND PATENT APPLICATIONS 119
5.12 PRICING ANALYSIS 123
5.12.1 PRICING DATA, BY OFFERING 124
5.12.2 PRICING DATA, BY PRODUCT TYPE 124
5.13 KEY CONFERENCES AND EVENTS, 2024–2025 125
5.14 PORTER’S FIVE FORCES ANALYSIS 126
5.14.1 THREAT OF NEW ENTRANTS 127
5.14.2 THREAT OF SUBSTITUTES 128
5.14.3 BARGAINING POWER OF SUPPLIERS 128
5.14.4 BARGAINING POWER OF BUYERS 128
5.14.5 INTENSITY OF COMPETITIVE RIVALRY 128
5.15 KEY STAKEHOLDERS AND BUYING CRITERIA 129
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS 129
5.15.2 BUYING CRITERIA 130
5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 131
6 AI TRAINING DATASET MARKET, BY OFFERING 132
6.1 INTRODUCTION 133
6.1.1 OFFERING: AI TRAINING DATASET MARKET DRIVERS 133
6.2 DATASET CREATION 134
6.2.1 DATASET CREATION KEY TO DEVELOPING ROBUST AI APPLICATIONS 134
6.3 DATASET SELLING 135
6.3.1 MONETIZING DATA FOR AI DEVELOPMENT THROUGH ETHICAL DATA SELLING 135
7 AI TRAINING DATASET MARKET, BY DATASET CREATION 137
7.1 INTRODUCTION 138
7.1.1 DATASET CREATION: AI TRAINING DATASET MARKET DRIVERS 138
7.2 DATASET CREATION SOFTWARE 140
7.2.1 DATASET CREATION SOFTWARE FUELING INNOVATIONS ACROSS VARIOUS SECTORS 140
7.2.2 DATA COLLECTION SOFTWARE 141
7.2.2.1 Web scraping tools 142
7.2.2.2 Data sourcing API 143
7.2.2.3 Crowdsourcing platforms 144
7.2.2.4 Sensor data collection software 145
7.2.3 DATA LABELING & ANNOTATION 146
7.2.3.1 Image annotation 147
7.2.3.2 Text annotation 148
7.2.3.3 Video annotation 149
7.2.3.4 Audio annotation 151
7.2.3.5 3D data annotation 152
7.2.4 SYNTHETIC DATA GENERATION SOFTWARE 153
7.2.5 DATA AUGMENTATION SOFTWARE 154
7.3 DATASET CREATION SERVICES 155
7.3.1 CUSTOMIZED DATA CREATION SERVICES FOR OPTIMAL AI MODEL ALIGNMENT 155
7.3.2 DATA COLLECTION SERVICES 156
7.3.3 DATA ANNOTATION & LABELING SERVICES 157
7.3.4 DATA VALIDATION SERVICES 158
8 AI TRAINING DATASET MARKET, BY DATASET SELLING 160
8.1 INTRODUCTION 161
8.1.1 DATASET SELLING: AI TRAINING DATASET MARKET DRIVERS 161
8.2 OFF-THE-SHELF (OTS) DATASETS 162
8.2.1 SCALABILITY AND EASE OF DISTRIBUTION MAKE OTS DATASETS APPEALING FOR AI TRAINING 162
8.3 DATASET MARKETPLACES 164
8.3.1 DATASET MARKETPLACES ACCELERATE AI INNOVATION BY DEMOCRATIZING ACCESS TO CRITICAL RESOURCES 164
9 AI TRAINING DATASET MARKET, BY ANNOTATION TYPE 165
9.1 INTRODUCTION 166
9.1.1 ANNOTATION TYPE: AI TRAINING DATASET MARKET DRIVERS 166
9.2 PRE-LABELED DATASETS 168
9.2.1 HIGH-QUALITY PRE-LABELED DATASETS ACCELERATE AI DEVELOPMENT ACROSS VARIOUS SECTORS 168
9.3 UNLABELED DATASETS 169
9.3.1 UNLABELED DATASETS ENABLE ROBUST AI MODEL TRAINING 169
9.4 SYNTHETIC DATASETS 170
9.4.1 ADVANCEMENTS IN GENERATIVE MODELS ENHANCE QUALITY OF SYNTHETIC DATASETS 170
10 AI TRAINING DATASET MARKET, BY DATA MODALITY 172
10.1 INTRODUCTION 173
10.1.1 DATA TYPE: AI TRAINING DATASET MARKET DRIVERS 173
10.2 TEXT 174
10.2.1 BUSINESSES PRIORITIZE CURATING DIVERSE, LABELED TEXT DATASETS TO ENHANCE MODEL ACCURACY 174
10.2.2 TEXT CLASSIFICATION 175
10.2.3 CHATBOTS 176
10.2.4 SENTIMENT ANALYSIS 177
10.2.5 DOCUMENT PARSING 178
10.2.6 OTHER TEXT DATA MODALITIES 179
10.3 IMAGE 181
10.3.1 ADVANCEMENTS IN DEEP LEARNING TECHNIQUES, PARTICULARLY CONVOLUTIONAL NEURAL NETWORKS, ELEVATE ROLE OF IMAGE DATA IN AI DEVELOPMENT 181
10.3.2 OBJECT DETECTION 182
10.3.3 FACIAL RECOGNITION 183
10.3.4 MEDICAL IMAGING 184
10.3.5 SATELLITE IMAGERY 185
10.3.6 OTHER IMAGE DATA MODALITIES 186
10.4 AUDIO & SPEECH 187
10.4.1 RISING POPULARITY OF VOICE-ACTIVATED TECHNOLOGIES FUELS DEMAND FOR DIVERSE, HIGH-QUALITY AUDIO DATASETS 187
10.4.2 SPEECH RECOGNITION 188
10.4.3 AUDIO CLASSIFICATION 189
10.4.4 MUSIC GENERATION 190
10.4.5 VOICE SYNTHESIS 191
10.4.6 OTHER AUDIO & SPEECH DATA MODALITIES 192
10.5 VIDEO 194
10.5.1 SURGE IN DEMAND FOR HIGH-QUALITY LABELED VIDEO DATASETS AS ORGANIZATIONS SEEK TO HARNESS VIDEO CONTENT POTENTIAL 194
10.5.2 ACTION RECOGNITION 195
10.5.3 AUTONOMOUS DRIVING 196
10.5.4 VIDEO SURVEILLANCE 197
10.5.5 VIDEO CONTENT MODERATION 198
10.5.6 OTHER VIDEO DATA MODALITIES 199
10.6 MULTIMODAL 200
10.6.1 RISING DEMAND FOR MULTIMODAL DATASETS BOOSTS INNOVATION AND ADVANCES IN AI APPLICATIONS 200
10.6.2 SPEECH-TO-TEXT 201
10.6.3 CONTENT RECOMMENDATION 202
10.6.4 VISUAL QUESTION ANSWERING (VQA) 203
10.6.5 MULTIMODAL ANALYTICS 204
10.6.6 OTHER MULTIMODALITIES 205
11 AI TRAINING DATASET MARKET, BY TYPE 207
11.1 INTRODUCTION 208
11.1.1 TYPE: AI TRAINING DATASET MARKET DRIVERS 208
11.2 GENERATIVE AI 210
11.2.1 GENERATIVE AI REVOLUTIONIZES CREATIVITY ACROSS INDUSTRIES THROUGH DIVERSE TRAINING DATASETS 210
11.2.2 LLM EVALUATION 211
11.2.3 RAG OPTIMIZATION 212
11.2.4 LLM FINE TUNING 214
11.2.5 CONVERSATIONAL AGENTS 215
11.2.6 CONTENT CREATION 216
11.2.7 CODE GENERATION 217
11.2.8 OTHER GENERATIVE AI 218
11.3 OTHER AI 219
11.3.1 RISING ROLE OF NLP AND COMPUTER VISION IN ENTERPRISE AI APPLICATIONS TO BOOST OTHER AI DATASET DEMAND 219
11.3.2 NATURAL LANGUAGE PROCESSING (NLP) 220
11.3.2.1 Text classification 221
11.3.2.2 Named entity recognition (NER) 222
11.3.2.3 Sentiment analysis 223
11.3.2.4 Document parsing and extraction 224
11.3.3 COMPUTER VISION 225
11.3.3.1 Image classification 226
11.3.3.2 Object detection 227
11.3.3.3 Video analysis 228
11.3.3.4 Optical character recognition (OCR) 229
11.3.4 PREDICTIVE ANALYTICS 230
11.3.4.1 Time series forecasting 232
11.3.4.2 Anomaly detection 233
11.3.4.3 Customer behavior prediction 234
11.3.4.4 Risk scoring and management 235
11.3.5 RECOMMENDATION SYSTEMS 236
11.3.5.1 Product and content recommendations 237
11.3.5.2 Personalized marketing and ads 238
11.3.5.3 Collaborative filtering 239
11.3.6 SPEECH AND AUDIO PROCESSING 240
11.3.6.1 Speech recognition 241
11.3.6.2 Audio classification 242
11.3.6.3 Voice command recognition 243
11.3.6.4 Speech-to-text transcription 244
11.3.7 OTHER TYPES 245
12 AI TRAINING DATASET MARKET, BY END USER 246
12.1 INTRODUCTION 247
12.1.1 END USER: AI TRAINING DATASET MARKET DRIVERS 247
12.2 BFSI 249
12.2.1 FINANCIAL INSTITUTIONS LEVERAGE AI TRAINING DATASETS TO ENHANCE FRAUD DETECTION AND RISK MANAGEMENT 249
12.2.2 BANKING 250
12.2.3 FINANCIAL SERVICES 251
12.2.4 INSURANCE 252
 
12.3 TELECOMMUNICATIONS 253
12.3.1 TELECOM COMPANIES BOOST PERFORMANCE AND CUSTOMER SERVICES WITH AI-POWERED INTELLIGENT SYSTEMS 253
12.4 GOVERNMENT & DEFENSE 254
12.4.1 AI TRAINING DATASETS PROPEL ADVANCES IN NATIONAL SECURITY AND DEFENSE OPERATIONS 254
12.5 HEALTHCARE & LIFE SCIENCES 256
12.5.1 AI TRAINING DATASETS SPEARHEAD TRANSFORMATIVE BREAKTHROUGHS IN PRECISION MEDICINE AND DIAGNOSTICS 256
12.6 MANUFACTURING 257
12.6.1 AI TRAINING DATASETS DRIVE EFFICIENCY IN MANUFACTURING WITH AUTOMATION AND PREDICTIVE MAINTENANCE 257
12.7 RETAIL & CONSUMER GOODS 258
12.7.1 RETAILERS ENHANCE PERSONALIZED CUSTOMER EXPERIENCES WITH AI-DRIVEN RECOMMENDATIONS AND OPTIMIZED SUPPLY CHAINS 258
12.8 SOFTWARE & TECHNOLOGY PROVIDERS 259
12.8.1 INNOVATION ACCELERATES AS SOFTWARE AND TECHNOLOGY PROVIDERS HARNESS AI TRAINING DATASETS FOR CUTTING-EDGE SOLUTIONS 259
12.8.2 CLOUD HYPERSCALERS 260
12.8.3 FOUNDATION MODEL/LLM PROVIDERS 261
12.8.4 AI TECHNOLOGY PROVIDERS 262
12.8.5 IT & IT-ENABLED SERVICE PROVIDERS 263
12.9 AUTOMOTIVE 264
12.9.1 RAPID ADVANCEMENTS IN AUTONOMOUS VEHICLE DEVELOPMENT FUELED BY AI TRAINING DATASETS CAPTURING REAL-WORLD DRIVING BEHAVIORS AND CONDITIONS 264
12.10 MEDIA & ENTERTAINMENT 265
12.10.1 AI TRAINING DATASETS FUEL INNOVATION IN CONTENT CREATION ACROSS MEDIA, GAMING, AND ENTERTAINMENT INDUSTRIES 265
12.11 OTHER END USERS 266
13 AI TRAINING DATASET MARKET, BY REGION 268
13.1 INTRODUCTION 269
13.2 NORTH AMERICA 270
13.2.1 NORTH AMERICA: AI TRAINING DATASET MARKET DRIVERS 271
13.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK 271
13.2.3 US 280
13.2.3.1 Reliance of companies across various sectors on large, diverse datasets to improve accuracy and performance of AI algorithms to drive market 280
13.2.4 CANADA 281
13.2.4.1 Government focus on gathering insights from stakeholders to maximize AI investment benefits to drive market 281
 
13.3 EUROPE 282
13.3.1 EUROPE: AI TRAINING DATASET MARKET DRIVERS 282
13.3.2 EUROPE: MACROECONOMIC OUTLOOK 283
13.3.3 UK 291
13.3.3.1 Rising demand for quality data and innovative solutions from various sectors to drive market 291
13.3.4 GERMANY 292
13.3.4.1 Industry demand, government support, and data privacy regulations to drive market 292
13.3.5 FRANCE 293
13.3.5.1 Increasing adoption of AI solutions by tech companies and startups to maintain competitive edge 293
13.3.6 ITALY 294
13.3.6.1 Advances in data collection and management enable companies to access diverse datasets tailored to various AI applications 294
13.3.7 SPAIN 295
13.3.7.1 Strategic government initiatives and industry innovation to drive market 295
13.3.8 NETHERLANDS 296
13.3.8.1 Focus on ethical AI and expanding digital infrastructure to accelerate demand for high-quality, diverse training datasets 296
13.3.9 REST OF EUROPE 297
13.4 ASIA PACIFIC 298
13.4.1 ASIA PACIFIC: AI TRAINING DATASET MARKET DRIVERS 298
13.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK 298
13.4.3 CHINA 308
13.4.3.1 Increasing demand for high-quality data for training models from various sectors to drive market 308
13.4.4 JAPAN 309
13.4.4.1 Supportive government policies and strategic corporate initiatives to drive market 309
13.4.5 INDIA 310
13.4.5.1 Increasing demand for AI solutions across various sectors to drive market 310
13.4.6 SOUTH KOREA 311
13.4.6.1 Increasing AI adoption and necessity for high-quality datasets to drive market 311
13.4.7 AUSTRALIA 312
13.4.7.1 Demand for quality data and ethical standards to drive market 312
13.4.8 SINGAPORE 313
13.4.8.1 Initiatives like Infocomm Media Development Authority (IMDA) promote data literacy and use of AI 313
13.4.9 REST OF ASIA PACIFIC 314
 
13.5 MIDDLE EAST & AFRICA 315
13.5.1 MIDDLE EAST & AFRICA: AI TRAINING DATASET MARKET DRIVERS 315
13.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 315
13.5.3 MIDDLE EAST 324
13.5.3.1 UAE 325
13.5.3.1.1 Initiatives by healthcare sector to build vast medical datasets for predictive analytics and disease detection to drive market 325
13.5.3.2 Saudi Arabia 326
13.5.3.2.1 Launch of Saudi Open Data Platform and partnership with global tech firms to accelerate AI training dataset development 326
13.5.3.3 Qatar 327
13.5.3.3.1 Strategic investments in startups specializing in streaming data to drive market 327
13.5.3.4 Turkey 328
13.5.3.4.1 Government initiatives and increasing demand for high-quality datasets from various sectors to drive market 328
13.5.3.5 Rest of Middle East 329
13.5.4 AFRICA 330
13.5.4.1 Increasing potential for AI application in various sectors to drive market 330
13.6 LATIN AMERICA 331
13.6.1 LATIN AMERICA: AI TRAINING DATASET MARKET DRIVERS 331
13.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK 332
13.6.3 BRAZIL 340
13.6.3.1 Growth in IT and healthcare sectors to drive market 340
13.6.4 MEXICO 341
13.6.4.1 Government initiatives and private sector investments to drive market 341
13.6.5 ARGENTINA 342
13.6.5.1 Government transparency initiatives and startup support to drive market 342
13.6.6 REST OF LATIN AMERICA 343
14 COMPETITIVE LANDSCAPE 344
14.1 OVERVIEW 344
14.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021–2024 344
14.3 REVENUE ANALYSIS, 2019–2023 347
14.4 MARKET SHARE ANALYSIS, 2023 349
14.4.1 MARKET RANKING ANALYSIS 350
14.5 PRODUCT COMPARATIVE ANALYSIS 352
14.5.1 AWS SAGEMAKER (AWS) 353
14.5.2 AI DATA PLATFORM (APPEN) 353
14.5.3 SAMA PLATFORM (SAMA) 353
14.5.4 DATA ENGINE, SCALE GEN AI PLATFORM (SCALE AI) 353
14.5.5 IMERIT PLATFORMS (IMERIT) 353
14.6 COMPANY VALUATION AND FINANCIAL METRICS, 2024 353
14.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 355
14.7.1 STARS 355
14.7.2 EMERGING LEADERS 355
14.7.3 PERVASIVE PLAYERS 355
14.7.4 PARTICIPANTS 355
14.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 357
14.7.5.1 Company footprint 357
14.7.5.2 Region footprint 358
14.7.5.3 Offering footprint 359
14.7.5.4 Data modality footprint 360
14.7.5.5 End user footprint 361
14.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 362
14.8.1 PROGRESSIVE COMPANIES 362
14.8.2 RESPONSIVE COMPANIES 362
14.8.3 DYNAMIC COMPANIES 362
14.8.4 STARTING BLOCKS 362
14.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 364
14.8.5.1 Detailed list of key startups/SMEs 364
14.8.5.2 Competitive benchmarking of key startups/SMEs 366
14.9 COMPETITIVE SCENARIO 367
14.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS 367
14.9.2 DEALS 370
15 COMPANY PROFILES 371
15.1 INTRODUCTION 371
15.2 KEY PLAYERS 371
15.2.1 GOOGLE 371
15.2.1.1 Business overview 371
15.2.1.2 Products/Solutions/Services offered 372
15.2.1.3 Recent developments 373
15.2.1.3.1 Product launches and enhancements 373
15.2.1.3.2 Deals 373
15.2.1.4 MnM view 374
15.2.1.4.1 Key strengths 374
15.2.1.4.2 Strategic choices 374
15.2.1.4.3 Weaknesses and competitive threats 374
 
15.2.2 MICROSOFT 375
15.2.2.1 Business overview 375
15.2.2.2 Products/Solutions/Services offered 376
15.2.2.3 Recent developments 377
15.2.2.3.1 Product launches and enhancements 377
15.2.2.4 MnM view 377
15.2.2.4.1 Key strengths 377
15.2.2.4.2 Strategic choices 377
15.2.2.4.3 Weaknesses and competitive threats 378
15.2.3 AWS 379
15.2.3.1 Business overview 379
15.2.3.2 Products/Solutions/Services offered 380
15.2.3.3 Recent developments 380
15.2.3.3.1 Product launches and enhancements 380
15.2.3.3.2 Deals 381
15.2.3.4 MnM view 381
15.2.3.4.1 Key strengths 381
15.2.3.4.2 Strategic choices 381
15.2.3.4.3 Weaknesses and competitive threats 381
15.2.4 APPEN 382
15.2.4.1 Business overview 382
15.2.4.2 Products/Solutions/Services offered 383
15.2.4.3 Recent developments 384
15.2.4.3.1 Product launches and enhancements 384
15.2.4.3.2 Deals 384
15.2.4.4 MnM view 385
15.2.4.4.1 Key strengths 385
15.2.4.4.2 Strategic choices 385
15.2.4.4.3 Weaknesses and competitive threats 385
15.2.5 NVIDIA 386
15.2.5.1 Business overview 386
15.2.5.2 Products/Solutions/Services offered 387
15.2.5.3 Recent developments 388
15.2.5.3.1 Product launches and enhancements 388
15.2.5.4 MnM view 388
15.2.5.4.1 Key strengths 388
15.2.5.4.2 Strategic choices 388
15.2.5.4.3 Weaknesses and competitive threats 389
15.2.6 IBM 390
15.2.6.1 Business overview 390
15.2.6.2 Products/Solutions/Services offered 391
 
15.2.7 TELUS INTERNATIONAL 392
15.2.7.1 Business overview 392
15.2.7.2 Products/Solutions/Services offered 393
15.2.8 INNODATA 394
15.2.8.1 Business overview 394
15.2.8.2 Products/Solutions/Services offered 395
15.2.8.3 Recent developments 396
15.2.8.3.1 Product launches and enhancements 396
15.2.9 COGITO TECH 397
15.2.9.1 Business overview 397
15.2.9.2 Products/Solutions/Services offered 398
15.2.10 SAMA 399
15.2.10.1 Business overview 399
15.2.10.2 Products/Solutions/Services offered 399
15.2.10.3 Recent developments 400
15.2.10.3.1 Product launches and enhancements 400
15.2.11 CLICKWORKER 401
15.2.12 TRANSPERFECT 401
15.2.13 CLOUDFACTORY 402
15.2.14 IMERIT 402
15.2.15 LIONBRIDGE TECHNOLOGIES 403
15.2.16 SCALE AI 404
15.3 STARTUPS/SMES 405
15.3.1 SNORKEL AI 405
15.3.2 GRETEL 406
15.3.3 SHAIP 407
15.3.4 NEXDATA 408
15.3.5 BITEXT 409
15.3.6 AIMLEAP 410
15.3.7 ALEGION 410
15.3.8 DEEP VISION DATA 411
15.3.9 LABELBOX 411
15.3.10 V7LABS 412
15.3.11 DEFINED.AI 413
15.3.12 SUPERANNOTATE 414
15.3.13 TOLOKA AI 414
15.3.14 KILI TECHNOLOGY 415
15.3.15 HUMANSIGNAL 415
15.3.16 SUPERB AI 416
15.3.17 HUGGING FACE 416
15.3.18 FILEMARKET 417
15.3.19 TAGX 418
15.3.20 ROBOFLOW 419
15.3.21 SUPERVISELY 419
15.3.22 ENCORD 420
15.3.23 KEYLABS 420
15.3.24 LXT 421
15.3.25 DATA.WORLD 421
16 ADJACENT AND RELATED MARKETS 422
16.1 INTRODUCTION 422
16.2 DATA ANNOTATION AND LABELING MARKET 422
16.2.1 MARKET DEFINITION 422
16.2.2 MARKET OVERVIEW 422
16.2.2.1 Data annotation and labeling market, by component 423
16.2.2.2 Data annotation and labeling market, by data type 424
16.2.2.3 Data annotation and labeling market, by deployment type 424
16.2.2.4 Data annotation and labeling market, by organization size 425
16.2.2.5 Data annotation and labeling market, by annotation type 426
16.2.2.6 Data annotation and labeling market, by application 427
16.2.2.7 Data annotation and labeling market, by vertical 429
16.2.2.8 Data annotation and labeling market, by region 430
16.3 SYNTHETIC DATA GENERATION MARKET 431
16.3.1 MARKET DEFINITION 431
16.3.2 MARKET OVERVIEW 431
16.3.2.1 Synthetic data generation market, by offering 431
16.3.2.2 Synthetic data generation market, by data type 432
16.3.2.3 Synthetic data generation market, by application 433
16.3.2.4 Synthetic data generation market, by vertical 434
16.3.2.5 Synthetic data generation market, by region 435
17 APPENDIX 437
17.1 DISCUSSION GUIDE 437
17.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 443
17.3 CUSTOMIZATION OPTIONS 445
17.4 RELATED REPORTS 445
17.5 AUTHOR DETAILS 446