세계의 AI 트레이닝 데이터세트 시장 : 제공 제품별, 데이터세트 작성별, 데이터세트 판매별, 어노테이션 유형별, 데이터 모달리티별, 유형별, 최종 사용자별, 지역별 - 예측(-2029년)
AI Training Dataset Market by Dataset Creation (Data Collection, Data Annotation, Synthetic Data Generation), Dataset Selling (Off-the-Shelf Datasets, Dataset Marketplaces), Data Modality (Text, Image, Video, Audio, Multimodal) - Global Forecast to 2029
AI 트레이닝 데이터세트 시장 규모는 2024년 28억 2,000만 달러로 추정되고, 2029년 95억 8,000만 달러로 증가할 전망이며, 2024년부터 2029년까지 연평균 복합 성장률(CAGR)은 27.7%가 될 것으로 예측됩니다.
다양한 분야들이 머신러닝과 AI 활용을 모색하고 있는 가운데, AI 교육 데이터 세트 수요는 급속히 증가하고 있습니다. 시장 성장을 가속하는 주요 요인은 특히 의료, 금융, 자율주행차 등 업계에서 AI 모델을 적절히 훈련하기 위한 일류의 다양한 데이터 수집에 대한 수요가 높아지고 있다는 것입니다. 그러나 데이터 프라이버시와 규정 준수에 대한 우려는 데이터 수집을 방해하고 개인 데이터에 대한 액세스를 제한할 수 있는 큰 장벽이 되고 있습니다. 기업은 혁신과 윤리적 요소를 조화시키면서 성능과 규제 요구 사항을 준수하는 데이터를 검색하고 관리하는 데 어려움을 겪고 있습니다.
| 조사 범위 |
| 조사 대상년도 |
2019-2029년 |
| 기준년 |
2023년 |
| 예측 기간 |
2024-2029년 |
| 검토 단위 |
달러(10억 달러) |
| 부문 |
제공 제품별, 데이터세트 작성별, 데이터세트 판매별, 어노테이션 유형별, 데이터 모달리티별, 유형별, 최종 사용자별, 지역별 |
| 대상 지역 |
북미, 유럽, 아시아태평양, 중동, 아프리카, 라틴아메리카 |
데이터세트 생성 부문은 다양한 업계에서 최고 수준의 데이터에 대한 요구가 증가함에 따라 예측 기간 동안 시장에서 가장 빠르게 성장할 것으로 예상됩니다. 기업은 데이터를 기반으로 의사 결정을 내리는 것의 중요성을 인식하고 있기 때문에 철저하고 정확한 데이터 세트 개발에 많은 투자를 하고 있습니다. 이 부분은 데이터 수집 및 처리를 간소화하여 기업이 더 빠르고 대규모로 데이터 세트를 만들 수 있도록 하는 AI와 ML의 발전을 활용합니다. 게다가 이 분야의 급성장에는 IoT 디바이스 증가와 디지털 상호작용으로부터 생성되는 데이터량 증가가 박차를 가하고 있습니다. 기업은 예측 분석을 수행하고 고객 행동을 이해하며 성과를 향상시키기 위한 재단사의 마케팅 전술을 고안하기 위해 대규모 데이터 세트를 만드는 것을 선호합니다. GDPR(EU 개인정보보호규정) 및 CCPA와 같은 규칙은 기업이 윤리적인 데이터 수집 방법에 초점을 맞추고 규제를 준수하는 맞춤형 데이터 세트에 대한 수요를 생성합니다. 기업은 각 업계에서 경쟁력을 유지하고 시장 성장을 느끼기 위해 특정 비즈니스 요구 사항을 충족하는 맞춤형 데이터 세트가 필요합니다.
OTS 데이터세트는 저렴하고 입수가 용이하며 다양한 용도에 즉각적으로 적합하기 때문에 데이터세트 판매 분야에서 시장을 선도할 것으로 예상됩니다. 데이터 수집 및 준비 시간을 절약하고 데이터 주도 전략의 신속한 도입을 가능하게 하기 위해 기업은 기성 데이터 세트를 선택하는 경우가 많습니다. 헬스케어, 금융, 마케팅 등 다양한 분야에서 데이터 분석에 대한 수요가 높아지고 있기 때문에 기업은 기존 데이터를 활용하여 의사결정을 개선하고 가치 있는 인사이트를 얻으려 하고 있으며, 이 동향은 더욱 가속화 하고 있습니다. 또한 인공지능과 머신러닝 기술의 등장으로 모델을 훈련하기위한 일류 데이터에 대한 수요가 높아지고 기성 데이터 세트에 대한 의존도가 높아지고 있습니다. 기업이 적응성과 경쟁력 유지를 우선시하기 때문에 기성 데이터 세트의 사용은 향후 몇 년동안 꾸준히 증가할 것으로 예상됩니다.
본 보고서에서는 세계 AI 트레이닝 데이터세트 시장을 조사했으며, 제공 제품별, 데이터세트 작성별, 데이터세트 판매별, 어노테이션 유형별, 데이터 모달리티별, 유형별, 최종 사용자별, 지역별 동향 및 시장에 참가하는 기업의 프로파일 등을 정리했습니다.
목차
제1장 서론
제2장 조사 방법
제3장 주요 요약
제4장 중요 인사이트
제5장 시장 개요 및 업계 동향
- 서문
- 시장 역학
- AI 트레이닝 데이터세트의 진화
- 공급망 분석
- 생태계 분석
- 투자 및 자금조달 시나리오
- 생성형 AI가 AI 트레이닝 데이터세트 시장에 미치는 영향
- 사례 연구 분석
- 기술 분석
- 규제 상황
- 특허 분석
- 가격 분석
- 주된 회의 및 이벤트(2024-2025년)
- Porter's Five Forces 분석
- 주요 이해관계자와 구매 기준
- 고객사업에 영향을 주는 동향 및 혼란
제6장 AI 트레이닝 데이터세트 시장 : 제공 제품별
제7장 AI 트레이닝 데이터세트 시장 : 데이터세트 작성별
- 서문
- 데이터세트 작성 소프트웨어
- 데이터세트 작성 서비스
제8장 AI 트레이닝 데이터세트 시장 : 데이터세트 판매별
- 서문
- 기성 데이터세트(OTS)
- 데이터세트 마켓플레이스
제9장 AI 트레이닝 데이터세트 시장 : 주석 유형별
- 서문
- 프리 라벨 데이터세트
- 라벨링된 데이터세트
- 합성 데이터세트
제10장 AI 트레이닝 데이터세트 시장 : 데이터 모달리티별
제11장 AI 트레이닝 데이터세트 시장 : 유형별
제12장 AI 트레이닝 데이터세트 시장 : 최종 사용자별
- 서문
- BFSI
- 통신
- 정부 및 방위
- 헬스케어 및 생명과학
- 제조
- 소매 및 소비재
- 소프트웨어 기술 공급자
- 자동차
- 미디어 및 엔터테인먼트
- 기타
제13장 AI 트레이닝 데이터세트 시장 : 지역별
- 서문
- 북미
- 유럽
- 아시아태평양
- 중동 및 아프리카
- 라틴아메리카
제14장 경쟁 구도
- 개요
- 주요 참가 기업의 전략 및 비책(2021-2024년)
- 수익 분석(2019-2023년)
- 시장 점유율 분석(2023년)
- 제품 비교 분석
- 기업평가 및 재무지표(2024년)
- 기업평가 매트릭스 : 주요 진입기업(2023년)
- 기업평가 매트릭스 : 스타트업 및 중소기업(2023년)
- 경쟁 시나리오
제15장 기업 프로파일
- 서문
- 주요 진출기업
- GOOGLE
- MICROSOFT
- AWS
- APPEN
- NVIDIA
- IBM
- TELUS INTERNATIONAL
- INNODATA
- COGITO TECH
- SAMA
- CLICKWORKER
- TRANSPERFECT
- CLOUDFACTORY
- IMERIT
- LIONBRIDGE TECHNOLOGIES
- SCALE AI
- 스타트업 및 중소기업
- SNORKEL AI
- GRETEL
- SHAIP
- NEXDATA
- BITEXT
- AIMLEAP
- ALEGION
- DEEP VISION DATA
- LABELBOX
- V7LABS
- DEFINED.AI
- SUPERANNOTATE
- TOLOKA AI
- KILI TECHNOLOGY
- HUMANSIGNAL
- SUPERB AI
- HUGGING FACE
- FILEMARKET
- TAGX
- ROBOFLOW
- SUPERVISELY
- ENCORD
- KEYLABS
- LXT
- DATA.WORLD
제16장 인접 시장과 관련 시장
제17장 부록
AJY
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.
| Scope of the Report |
|---|
| Years Considered for the Study | 2019-2029 |
| Base Year | 2023 |
| Forecast Period | 2024-2029 |
| Units Considered | USD (Billion) |
| Segments | Offering, Dataset Creation, Dataset Selling, Type, Data Modality, Annotation Type, End User, and Region |
| Regions covered | North America, Europe, Asia Pacific, Middle East & Africa, and Latin America |
"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.
TABLE OF CONTENTS
1 INTRODUCTION
- 1.1 STUDY OBJECTIVES
- 1.2 MARKET DEFINITION
- 1.2.1 INCLUSIONS AND EXCLUSIONS
- 1.3 MARKET SCOPE
- 1.3.1 MARKET SEGMENTATION
- 1.3.2 YEARS CONSIDERED
- 1.4 CURRENCY CONSIDERED
- 1.5 STAKEHOLDERS
2 RESEARCH METHODOLOGY
- 2.1 RESEARCH DATA
- 2.1.1 SECONDARY DATA
- 2.1.2 PRIMARY DATA
- 2.1.2.1 Breakup of primary profiles
- 2.1.2.2 Key industry insights
- 2.2 MARKET BREAKUP AND DATA TRIANGULATION
- 2.3 MARKET SIZE ESTIMATION
- 2.3.1 TOP-DOWN APPROACH
- 2.3.2 BOTTOM-UP APPROACH
- 2.4 MARKET FORECAST
- 2.5 RESEARCH ASSUMPTIONS
- 2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
- 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI TRAINING DATASET MARKET
- 4.2 AI TRAINING DATASET MARKET, BY TOP THREE DATA MODALITIES
- 4.3 NORTH AMERICA: AI TRAINING DATASET MARKET, BY ANNOTATION TYPE AND END USER
- 4.4 AI TRAINING DATASET MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
- 5.1 INTRODUCTION
- 5.2 MARKET DYNAMICS
- 5.2.1 DRIVERS
- 5.2.1.1 Increasing need for diverse and continuously updated multimodal datasets for generative AI models
- 5.2.1.2 Rising use of multilingual datasets in conversational AI
- 5.2.1.3 Growing demand for high-quality labeled data for autonomous vehicles
- 5.2.1.4 Rising adoption of synthetic data for rare event simulation
- 5.2.2 RESTRAINTS
- 5.2.2.1 Legal risks of web-scraped data due to copyright infringement
- 5.2.2.2 Limited access to high-quality medical datasets due to HIPAA compliance
- 5.2.3 OPPORTUNITIES
- 5.2.3.1 Growing demand for specialized data annotation services in diverse fields
- 5.2.3.2 Synthetic data generation and privacy-preserving techniques for augmented training data
- 5.2.3.3 Creation of customized AI datasets and specialized formats for enterprise solutions
- 5.2.4 CHALLENGES
- 5.2.4.1 Data quality and relevance issues
- 5.2.4.2 Diverse dataset formats and inconsistent annotation practices
- 5.3 EVOLUTION OF AI TRAINING DATASET
- 5.4 SUPPLY CHAIN ANALYSIS
- 5.5 ECOSYSTEM ANALYSIS
- 5.5.1 DATA COLLECTION SOFTWARE PROVIDERS
- 5.5.2 DATA LABELING AND ANNOTATION PLATFORM PROVIDERS
- 5.5.3 SYNTHETIC DATA PROVIDERS
- 5.5.4 DATA AUGMENTATION TOOL PROVIDERS
- 5.5.5 OFF-THE-SHELF (OTS) DATASET PROVIDERS
- 5.5.6 AI TRAINING DATASET SERVICE PROVIDERS
- 5.6 INVESTMENT AND FUNDING SCENARIO
- 5.7 IMPACT OF GENERATIVE AI ON AI TRAINING DATASET MARKET
- 5.7.1 DATA AUGMENTATION FOR IMAGE RECOGNITION
- 5.7.2 SYNTHETIC TEXT GENERATION FOR NLP
- 5.7.3 SPEECH AND AUDIO DATA SYNTHESIS
- 5.7.4 SIMULATED USER INTERACTION DATA
- 5.7.5 BIAS MITIGATION IN DATASETS
- 5.7.6 SCENARIO TESTING FOR PREDICTIVE MODELS
- 5.8 CASE STUDY ANALYSIS
- 5.8.1 CASE STUDY 1: CLICKWORKER BOOSTS AI TRAINING DATASET FOR AUTOMOTIVE SYSTEMS, IMPROVING SPEECH RECOGNITION ACCURACY
- 5.8.2 CASE STUDY 2: APPEN ENHANCES MICROSOFT TRANSLATOR WITH COMPREHENSIVE AI TRAINING DATASETS FOR 110 LANGUAGES
- 5.8.3 CASE STUDY 3: COGITO TECH LLC ENHANCES CARDIAC SURGERY WITH AI-DRIVEN AORTIC VALVE DATASETS
- 5.8.4 CASE STUDY 4: ENHANCING AI TRAINING DATASETS FOR PAIN REDUCTION THROUGH HINGE HEALTH'S SUCCESS WITH SUPERANNOTATE
- 5.8.5 CASE STUDY 5: OUTREACH ENHANCES AI TRAINING WITH LABEL STUDIO
- 5.8.6 CASE STUDY 6: ENCORD ADDRESSES KEY CHALLENGES IN SURGICAL VIDEO ANNOTATION FOR ENHANCED DATA QUALITY AND EFFICIENCY
- 5.9 TECHNOLOGY ANALYSIS
- 5.9.1 KEY TECHNOLOGIES
- 5.9.1.1 Data labeling and annotation
- 5.9.1.2 Synthetic data generation
- 5.9.1.3 Data augmentation
- 5.9.1.4 Human-in-the-loop (HITL) feedback systems
- 5.9.1.5 Active learning
- 5.9.1.6 Data cleansing and preprocessing
- 5.9.1.7 Bias detection and mitigation
- 5.9.1.8 Dataset versioning and management
- 5.9.2 COMPLEMENTARY TECHNOLOGIES
- 5.9.2.1 Cloud storage and data lakes
- 5.9.2.2 MLOps and model management
- 5.9.2.3 Data governance
- 5.9.2.4 Machine learning frameworks
- 5.9.3 ADJACENT TECHNOLOGIES
- 5.9.3.1 Federated learning
- 5.9.3.2 Edge AI for data processing
- 5.9.3.3 Differential privacy
- 5.9.3.4 AutoML
- 5.9.3.5 Transfer learning
- 5.10 REGULATORY LANDSCAPE
- 5.10.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
- 5.10.2 REGULATIONS: AI TRAINING DATASET
- 5.10.2.1 North America
- 5.10.2.1.1 Blueprint for an AI Bill of Rights (US)
- 5.10.2.1.2 Directive on Automated Decision-Making (Canada)
- 5.10.2.2 Europe
- 5.10.2.2.1 UK AI Regulation White Paper
- 5.10.2.2.2 Gesetz zur Regulierung Kunstlicher Intelligenz (AI Regulation Law - Germany)
- 5.10.2.2.3 Loi pour une Republique numerique (Digital Republic Act - France)
- 5.10.2.2.4 Codice in materia di protezione dei dati personali (Data Protection Code - Italy)
- 5.10.2.2.5 Ley de Servicios Digitales (Digital Services Act - Spain)
- 5.10.2.2.6 Dutch Data Protection Authority (Autoriteit Persoonsgegevens) Guidelines
- 5.10.2.2.7 The Swedish National Board of Trade AI Guidelines
- 5.10.2.2.8 Danish Data Protection Agency (Datatilsynet) AI Recommendations
- 5.10.2.2.9 Artificial Intelligence 4.0 (AI 4.0) Program - Finland
- 5.10.2.3 Asia Pacific
- 5.10.2.3.1 Personal Data Protection Bill (PDPB) & National Strategy on AI (NSAI) - India
- 5.10.2.3.2 The Basic Act on the Advancement of Utilizing Public and Private Sector Data & AI Guidelines - Japan
- 5.10.2.3.3 New Generation Artificial Intelligence Development Plan & AI Ethics Guidelines - China
- 5.10.2.3.4 Framework Act on Intelligent Informatization - South Korea
- 5.10.2.3.5 AI Ethics Framework (Australia) & AI Strategy (New Zealand)
- 5.10.2.3.6 Model AI Governance Framework - Singapore
- 5.10.2.3.7 National AI Framework - Malaysia
- 5.10.2.3.8 National AI Roadmap - Philippines
- 5.10.2.4 Middle East & Africa
- 5.10.2.4.1 Saudi Data & Artificial Intelligence Authority (SDAIA) Regulations
- 5.10.2.4.2 UAE National AI Strategy 2031
- 5.10.2.4.3 Qatar National AI Strategy
- 5.10.2.4.4 National Artificial Intelligence Strategy (2021-2025)- Turkey
- 5.10.2.4.5 African Union (AU) AI Framework
- 5.10.2.4.6 Egyptian Artificial Intelligence Strategy
- 5.10.2.4.7 Kuwait National Development Plan (New Kuwait Vision 2035)
- 5.10.2.5 Latin America
- 5.10.2.5.1 Brazilian General Data Protection Law (LGPD)
- 5.10.2.5.2 Federal Law on the Protection of Personal Data Held by Private Parties - Mexico
- 5.10.2.5.3 Argentina Personal Data Protection Law (PDPL) & AI Ethics Framework
- 5.10.2.5.4 Chilean Data Protection Law & National AI Policy
- 5.10.2.5.5 Colombian Data Protection Law (Law 1581) & AI Ethics Guidelines
- 5.10.2.5.6 Peruvian Personal Data Protection Law & National AI Strategy
- 5.11 PATENT ANALYSIS
- 5.11.1 METHODOLOGY
- 5.11.2 PATENTS FILED, BY DOCUMENT TYPE
- 5.11.3 INNOVATION AND PATENT APPLICATIONS
- 5.12 PRICING ANALYSIS
- 5.12.1 PRICING DATA, BY OFFERING
- 5.12.2 PRICING DATA, BY PRODUCT TYPE
- 5.13 KEY CONFERENCES AND EVENTS, 2024-2025
- 5.14 PORTER'S FIVE FORCES ANALYSIS
- 5.14.1 THREAT OF NEW ENTRANTS
- 5.14.2 THREAT OF SUBSTITUTES
- 5.14.3 BARGAINING POWER OF SUPPLIERS
- 5.14.4 BARGAINING POWER OF BUYERS
- 5.14.5 INTENSITY OF COMPETITIVE RIVALRY
- 5.15 KEY STAKEHOLDERS AND BUYING CRITERIA
- 5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
- 5.15.2 BUYING CRITERIA
- 5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
6 AI TRAINING DATASET MARKET, BY OFFERING
- 6.1 INTRODUCTION
- 6.1.1 OFFERING: AI TRAINING DATASET MARKET DRIVERS
- 6.2 DATASET CREATION
- 6.2.1 DATASET CREATION KEY TO DEVELOPING ROBUST AI APPLICATIONS
- 6.3 DATASET SELLING
- 6.3.1 MONETIZING DATA FOR AI DEVELOPMENT THROUGH ETHICAL DATA SELLING
7 AI TRAINING DATASET MARKET, BY DATASET CREATION
- 7.1 INTRODUCTION
- 7.1.1 DATASET CREATION: AI TRAINING DATASET MARKET DRIVERS
- 7.2 DATASET CREATION SOFTWARE
- 7.2.1 DATASET CREATION SOFTWARE FUELING INNOVATIONS ACROSS VARIOUS SECTORS
- 7.2.2 DATA COLLECTION SOFTWARE
- 7.2.2.1 Web scraping tools
- 7.2.2.2 Data sourcing API
- 7.2.2.3 Crowdsourcing platforms
- 7.2.2.4 Sensor data collection software
- 7.2.3 DATA LABELING & ANNOTATION
- 7.2.3.1 Image annotation
- 7.2.3.2 Text annotation
- 7.2.3.3 Video annotation
- 7.2.3.4 Audio annotation
- 7.2.3.5 3D data annotation
- 7.2.4 SYNTHETIC DATA GENERATION SOFTWARE
- 7.2.5 DATA AUGMENTATION SOFTWARE
- 7.3 DATASET CREATION SERVICES
- 7.3.1 CUSTOMIZED DATA CREATION SERVICES FOR OPTIMAL AI MODEL ALIGNMENT
- 7.3.2 DATA COLLECTION SERVICES
- 7.3.3 DATA ANNOTATION & LABELING SERVICES
- 7.3.4 DATA VALIDATION SERVICES
8 AI TRAINING DATASET MARKET, BY DATASET SELLING
- 8.1 INTRODUCTION
- 8.1.1 DATASET SELLING: AI TRAINING DATASET MARKET DRIVERS
- 8.2 OFF-THE-SHELF (OTS) DATASETS
- 8.2.1 SCALABILITY AND EASE OF DISTRIBUTION MAKE OTS DATASETS APPEALING FOR AI TRAINING
- 8.3 DATASET MARKETPLACES
- 8.3.1 DATASET MARKETPLACES ACCELERATE AI INNOVATION BY DEMOCRATIZING ACCESS TO CRITICAL RESOURCES
9 AI TRAINING DATASET MARKET, BY ANNOTATION TYPE
- 9.1 INTRODUCTION
- 9.1.1 ANNOTATION TYPE: AI TRAINING DATASET MARKET DRIVERS
- 9.2 PRE-LABELED DATASETS
- 9.2.1 HIGH-QUALITY PRE-LABELED DATASETS ACCELERATE AI DEVELOPMENT ACROSS VARIOUS SECTORS
- 9.3 UNLABELED DATASETS
- 9.3.1 UNLABELED DATASETS ENABLE ROBUST AI MODEL TRAINING
- 9.4 SYNTHETIC DATASETS
- 9.4.1 ADVANCEMENTS IN GENERATIVE MODELS ENHANCE QUALITY OF SYNTHETIC DATASETS
10 AI TRAINING DATASET MARKET, BY DATA MODALITY
- 10.1 INTRODUCTION
- 10.1.1 DATA TYPE: AI TRAINING DATASET MARKET DRIVERS
- 10.2 TEXT
- 10.2.1 BUSINESSES PRIORITIZE CURATING DIVERSE, LABELED TEXT DATASETS TO ENHANCE MODEL ACCURACY
- 10.2.2 TEXT CLASSIFICATION
- 10.2.3 CHATBOTS
- 10.2.4 SENTIMENT ANALYSIS
- 10.2.5 DOCUMENT PARSING
- 10.2.6 OTHER TEXT DATA MODALITIES
- 10.3 IMAGE
- 10.3.1 ADVANCEMENTS IN DEEP LEARNING TECHNIQUES, PARTICULARLY CONVOLUTIONAL NEURAL NETWORKS, ELEVATE ROLE OF IMAGE DATA IN AI DEVELOPMENT
- 10.3.2 OBJECT DETECTION
- 10.3.3 FACIAL RECOGNITION
- 10.3.4 MEDICAL IMAGING
- 10.3.5 SATELLITE IMAGERY
- 10.3.6 OTHER IMAGE DATA MODALITIES
- 10.4 AUDIO & SPEECH
- 10.4.1 RISING POPULARITY OF VOICE-ACTIVATED TECHNOLOGIES FUELS DEMAND FOR DIVERSE, HIGH-QUALITY AUDIO DATASETS
- 10.4.2 SPEECH RECOGNITION
- 10.4.3 AUDIO CLASSIFICATION
- 10.4.4 MUSIC GENERATION
- 10.4.5 VOICE SYNTHESIS
- 10.4.6 OTHER AUDIO & SPEECH DATA MODALITIES
- 10.5 VIDEO
- 10.5.1 SURGE IN DEMAND FOR HIGH-QUALITY LABELED VIDEO DATASETS AS ORGANIZATIONS SEEK TO HARNESS VIDEO CONTENT POTENTIAL
- 10.5.2 ACTION RECOGNITION
- 10.5.3 AUTONOMOUS DRIVING
- 10.5.4 VIDEO SURVEILLANCE
- 10.5.5 VIDEO CONTENT MODERATION
- 10.5.6 OTHER VIDEO DATA MODALITIES
- 10.6 MULTIMODAL
- 10.6.1 RISING DEMAND FOR MULTIMODAL DATASETS BOOSTS INNOVATION AND ADVANCES IN AI APPLICATIONS
- 10.6.2 SPEECH-TO-TEXT
- 10.6.3 CONTENT RECOMMENDATION
- 10.6.4 VISUAL QUESTION ANSWERING (VQA)
- 10.6.5 MULTIMODAL ANALYTICS
- 10.6.6 OTHER MULTIMODALITIES
11 AI TRAINING DATASET MARKET, BY TYPE
- 11.1 INTRODUCTION
- 11.1.1 TYPE: AI TRAINING DATASET MARKET DRIVERS
- 11.2 GENERATIVE AI
- 11.2.1 GENERATIVE AI REVOLUTIONIZES CREATIVITY ACROSS INDUSTRIES THROUGH DIVERSE TRAINING DATASETS
- 11.2.2 LLM EVALUATION
- 11.2.3 RAG OPTIMIZATION
- 11.2.4 LLM FINE TUNING
- 11.2.5 CONVERSATIONAL AGENTS
- 11.2.6 CONTENT CREATION
- 11.2.7 CODE GENERATION
- 11.2.8 OTHER GENERATIVE AI
- 11.3 OTHER AI
- 11.3.1 RISING ROLE OF NLP AND COMPUTER VISION IN ENTERPRISE AI APPLICATIONS TO BOOST OTHER AI DATASET DEMAND
- 11.3.2 NATURAL LANGUAGE PROCESSING (NLP)
- 11.3.2.1 Text classification
- 11.3.2.2 Named entity recognition (NER)
- 11.3.2.3 Sentiment analysis
- 11.3.2.4 Document parsing and extraction
- 11.3.3 COMPUTER VISION
- 11.3.3.1 Image classification
- 11.3.3.2 Object detection
- 11.3.3.3 Video analysis
- 11.3.3.4 Optical character recognition (OCR)
- 11.3.4 PREDICTIVE ANALYTICS
- 11.3.4.1 Time series forecasting
- 11.3.4.2 Anomaly detection
- 11.3.4.3 Customer behavior prediction
- 11.3.4.4 Risk scoring and management
- 11.3.5 RECOMMENDATION SYSTEMS
- 11.3.5.1 Product and content recommendations
- 11.3.5.2 Personalized marketing and ads
- 11.3.5.3 Collaborative filtering
- 11.3.6 SPEECH AND AUDIO PROCESSING
- 11.3.6.1 Speech recognition
- 11.3.6.2 Audio classification
- 11.3.6.3 Voice command recognition
- 11.3.6.4 Speech-to-text transcription
- 11.3.7 OTHER TYPES
12 AI TRAINING DATASET MARKET, BY END USER
- 12.1 INTRODUCTION
- 12.1.1 END USER: AI TRAINING DATASET MARKET DRIVERS
- 12.2 BFSI
- 12.2.1 FINANCIAL INSTITUTIONS LEVERAGE AI TRAINING DATASETS TO ENHANCE FRAUD DETECTION AND RISK MANAGEMENT
- 12.2.2 BANKING
- 12.2.3 FINANCIAL SERVICES
- 12.2.4 INSURANCE
- 12.3 TELECOMMUNICATIONS
- 12.3.1 TELECOM COMPANIES BOOST PERFORMANCE AND CUSTOMER SERVICES WITH AI-POWERED INTELLIGENT SYSTEMS
- 12.4 GOVERNMENT & DEFENSE
- 12.4.1 AI TRAINING DATASETS PROPEL ADVANCES IN NATIONAL SECURITY AND DEFENSE OPERATIONS
- 12.5 HEALTHCARE & LIFE SCIENCES
- 12.5.1 AI TRAINING DATASETS SPEARHEAD TRANSFORMATIVE BREAKTHROUGHS IN PRECISION MEDICINE AND DIAGNOSTICS
- 12.6 MANUFACTURING
- 12.6.1 AI TRAINING DATASETS DRIVE EFFICIENCY IN MANUFACTURING WITH AUTOMATION AND PREDICTIVE MAINTENANCE
- 12.7 RETAIL & CONSUMER GOODS
- 12.7.1 RETAILERS ENHANCE PERSONALIZED CUSTOMER EXPERIENCES WITH AI-DRIVEN RECOMMENDATIONS AND OPTIMIZED SUPPLY CHAINS
- 12.8 SOFTWARE & TECHNOLOGY PROVIDERS
- 12.8.1 INNOVATION ACCELERATES AS SOFTWARE AND TECHNOLOGY PROVIDERS HARNESS AI TRAINING DATASETS FOR CUTTING-EDGE SOLUTIONS
- 12.8.2 CLOUD HYPERSCALERS
- 12.8.3 FOUNDATION MODEL/LLM PROVIDERS
- 12.8.4 AI TECHNOLOGY PROVIDERS
- 12.8.5 IT & IT-ENABLED SERVICE PROVIDERS
- 12.9 AUTOMOTIVE
- 12.9.1 RAPID ADVANCEMENTS IN AUTONOMOUS VEHICLE DEVELOPMENT FUELED BY AI TRAINING DATASETS CAPTURING REAL-WORLD DRIVING BEHAVIORS AND CONDITIONS
- 12.10 MEDIA & ENTERTAINMENT
- 12.10.1 AI TRAINING DATASETS FUEL INNOVATION IN CONTENT CREATION ACROSS MEDIA, GAMING, AND ENTERTAINMENT INDUSTRIES
- 12.11 OTHER END USERS
13 AI TRAINING DATASET MARKET, BY REGION
- 13.1 INTRODUCTION
- 13.2 NORTH AMERICA
- 13.2.1 NORTH AMERICA: AI TRAINING DATASET MARKET DRIVERS
- 13.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
- 13.2.3 US
- 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
- 13.2.4 CANADA
- 13.2.4.1 Government focus on gathering insights from stakeholders to maximize AI investment benefits to drive market
- 13.3 EUROPE
- 13.3.1 EUROPE: AI TRAINING DATASET MARKET DRIVERS
- 13.3.2 EUROPE: MACROECONOMIC OUTLOOK
- 13.3.3 UK
- 13.3.3.1 Rising demand for quality data and innovative solutions from various sectors to drive market
- 13.3.4 GERMANY
- 13.3.4.1 Industry demand, government support, and data privacy regulations to drive market
- 13.3.5 FRANCE
- 13.3.5.1 Increasing adoption of AI solutions by tech companies and startups to maintain competitive edge
- 13.3.6 ITALY
- 13.3.6.1 Advances in data collection and management enable companies to access diverse datasets tailored to various AI applications
- 13.3.7 SPAIN
- 13.3.7.1 Strategic government initiatives and industry innovation to drive market
- 13.3.8 NETHERLANDS
- 13.3.8.1 Focus on ethical AI and expanding digital infrastructure to accelerate demand for high-quality, diverse training datasets
- 13.3.9 REST OF EUROPE
- 13.4 ASIA PACIFIC
- 13.4.1 ASIA PACIFIC: AI TRAINING DATASET MARKET DRIVERS
- 13.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
- 13.4.3 CHINA
- 13.4.3.1 Increasing demand for high-quality data for training models from various sectors to drive market
- 13.4.4 JAPAN
- 13.4.4.1 Supportive government policies and strategic corporate initiatives to drive market
- 13.4.5 INDIA
- 13.4.5.1 Increasing demand for AI solutions across various sectors to drive market
- 13.4.6 SOUTH KOREA
- 13.4.6.1 Increasing AI adoption and necessity for high-quality datasets to drive market
- 13.4.7 AUSTRALIA
- 13.4.7.1 Demand for quality data and ethical standards to drive market
- 13.4.8 SINGAPORE
- 13.4.8.1 Initiatives like Infocomm Media Development Authority (IMDA) promote data literacy and use of AI
- 13.4.9 REST OF ASIA PACIFIC
- 13.5 MIDDLE EAST & AFRICA
- 13.5.1 MIDDLE EAST & AFRICA: AI TRAINING DATASET MARKET DRIVERS
- 13.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
- 13.5.3 MIDDLE EAST
- 13.5.3.1 UAE
- 13.5.3.1.1 Initiatives by healthcare sector to build vast medical datasets for predictive analytics and disease detection to drive market
- 13.5.3.2 Saudi Arabia
- 13.5.3.2.1 Launch of Saudi Open Data Platform and partnership with global tech firms to accelerate AI training dataset development
- 13.5.3.3 Qatar
- 13.5.3.3.1 Strategic investments in startups specializing in streaming data to drive market
- 13.5.3.4 Turkey
- 13.5.3.4.1 Government initiatives and increasing demand for high-quality datasets from various sectors to drive market
- 13.5.3.5 Rest of Middle East
- 13.5.4 AFRICA
- 13.5.4.1 Increasing potential for AI application in various sectors to drive market
- 13.6 LATIN AMERICA
- 13.6.1 LATIN AMERICA: AI TRAINING DATASET MARKET DRIVERS
- 13.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
- 13.6.3 BRAZIL
- 13.6.3.1 Growth in IT and healthcare sectors to drive market
- 13.6.4 MEXICO
- 13.6.4.1 Government initiatives and private sector investments to drive market
- 13.6.5 ARGENTINA
- 13.6.5.1 Government transparency initiatives and startup support to drive market
- 13.6.6 REST OF LATIN AMERICA
14 COMPETITIVE LANDSCAPE
- 14.1 OVERVIEW
- 14.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021-2024
- 14.3 REVENUE ANALYSIS, 2019-2023
- 14.4 MARKET SHARE ANALYSIS, 2023
- 14.4.1 MARKET RANKING ANALYSIS
- 14.5 PRODUCT COMPARATIVE ANALYSIS
- 14.5.1 AWS SAGEMAKER (AWS)
- 14.5.2 AI DATA PLATFORM (APPEN)
- 14.5.3 SAMA PLATFORM (SAMA)
- 14.5.4 DATA ENGINE, SCALE GEN AI PLATFORM (SCALE AI)
- 14.5.5 IMERIT PLATFORMS (IMERIT)
- 14.6 COMPANY VALUATION AND FINANCIAL METRICS, 2024
- 14.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
- 14.7.1 STARS
- 14.7.2 EMERGING LEADERS
- 14.7.3 PERVASIVE PLAYERS
- 14.7.4 PARTICIPANTS
- 14.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
- 14.7.5.1 Company footprint
- 14.7.5.2 Region footprint
- 14.7.5.3 Offering footprint
- 14.7.5.4 Data modality footprint
- 14.7.5.5 End user footprint
- 14.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
- 14.8.1 PROGRESSIVE COMPANIES
- 14.8.2 RESPONSIVE COMPANIES
- 14.8.3 DYNAMIC COMPANIES
- 14.8.4 STARTING BLOCKS
- 14.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
- 14.8.5.1 Detailed list of key startups/SMEs
- 14.8.5.2 Competitive benchmarking of key startups/SMEs
- 14.9 COMPETITIVE SCENARIO
- 14.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
- 14.9.2 DEALS
15 COMPANY PROFILES
- 15.1 INTRODUCTION
- 15.2 KEY PLAYERS
- 15.2.1 GOOGLE
- 15.2.1.1 Business overview
- 15.2.1.2 Products/Solutions/Services offered
- 15.2.1.3 Recent developments
- 15.2.1.3.1 Product launches and enhancements
- 15.2.1.3.2 Deals
- 15.2.1.4 MnM view
- 15.2.1.4.1 Key strengths
- 15.2.1.4.2 Strategic choices
- 15.2.1.4.3 Weaknesses and competitive threats
- 15.2.2 MICROSOFT
- 15.2.2.1 Business overview
- 15.2.2.2 Products/Solutions/Services offered
- 15.2.2.3 Recent developments
- 15.2.2.3.1 Product launches and enhancements
- 15.2.2.4 MnM view
- 15.2.2.4.1 Key strengths
- 15.2.2.4.2 Strategic choices
- 15.2.2.4.3 Weaknesses and competitive threats
- 15.2.3 AWS
- 15.2.3.1 Business overview
- 15.2.3.2 Products/Solutions/Services offered
- 15.2.3.3 Recent developments
- 15.2.3.3.1 Product launches and enhancements
- 15.2.3.3.2 Deals
- 15.2.3.4 MnM view
- 15.2.3.4.1 Key strengths
- 15.2.3.4.2 Strategic choices
- 15.2.3.4.3 Weaknesses and competitive threats
- 15.2.4 APPEN
- 15.2.4.1 Business overview
- 15.2.4.2 Products/Solutions/Services offered
- 15.2.4.3 Recent developments
- 15.2.4.3.1 Product launches and enhancements
- 15.2.4.3.2 Deals
- 15.2.4.4 MnM view
- 15.2.4.4.1 Key strengths
- 15.2.4.4.2 Strategic choices
- 15.2.4.4.3 Weaknesses and competitive threats
- 15.2.5 NVIDIA
- 15.2.5.1 Business overview
- 15.2.5.2 Products/Solutions/Services offered
- 15.2.5.3 Recent developments
- 15.2.5.3.1 Product launches and enhancements
- 15.2.5.4 MnM view
- 15.2.5.4.1 Key strengths
- 15.2.5.4.2 Strategic choices
- 15.2.5.4.3 Weaknesses and competitive threats
- 15.2.6 IBM
- 15.2.6.1 Business overview
- 15.2.6.2 Products/Solutions/Services offered
- 15.2.7 TELUS INTERNATIONAL
- 15.2.7.1 Business overview
- 15.2.7.2 Products/Solutions/Services offered
- 15.2.8 INNODATA
- 15.2.8.1 Business overview
- 15.2.8.2 Products/Solutions/Services offered
- 15.2.8.3 Recent developments
- 15.2.8.3.1 Product launches and enhancements
- 15.2.9 COGITO TECH
- 15.2.9.1 Business overview
- 15.2.9.2 Products/Solutions/Services offered
- 15.2.10 SAMA
- 15.2.10.1 Business overview
- 15.2.10.2 Products/Solutions/Services offered
- 15.2.10.3 Recent developments
- 15.2.10.3.1 Product launches and enhancements
- 15.2.11 CLICKWORKER
- 15.2.12 TRANSPERFECT
- 15.2.13 CLOUDFACTORY
- 15.2.14 IMERIT
- 15.2.15 LIONBRIDGE TECHNOLOGIES
- 15.2.16 SCALE AI
- 15.3 STARTUPS/SMES
- 15.3.1 SNORKEL AI
- 15.3.2 GRETEL
- 15.3.3 SHAIP
- 15.3.4 NEXDATA
- 15.3.5 BITEXT
- 15.3.6 AIMLEAP
- 15.3.7 ALEGION
- 15.3.8 DEEP VISION DATA
- 15.3.9 LABELBOX
- 15.3.10 V7LABS
- 15.3.11 DEFINED.AI
- 15.3.12 SUPERANNOTATE
- 15.3.13 TOLOKA AI
- 15.3.14 KILI TECHNOLOGY
- 15.3.15 HUMANSIGNAL
- 15.3.16 SUPERB AI
- 15.3.17 HUGGING FACE
- 15.3.18 FILEMARKET
- 15.3.19 TAGX
- 15.3.20 ROBOFLOW
- 15.3.21 SUPERVISELY
- 15.3.22 ENCORD
- 15.3.23 KEYLABS
- 15.3.24 LXT
- 15.3.25 DATA.WORLD
16 ADJACENT AND RELATED MARKETS
- 16.1 INTRODUCTION
- 16.2 DATA ANNOTATION AND LABELING MARKET
- 16.2.1 MARKET DEFINITION
- 16.2.2 MARKET OVERVIEW
- 16.2.2.1 Data annotation and labeling market, by component
- 16.2.2.2 Data annotation and labeling market, by data type
- 16.2.2.3 Data annotation and labeling market, by deployment type
- 16.2.2.4 Data annotation and labeling market, by organization size
- 16.2.2.5 Data annotation and labeling market, by annotation type
- 16.2.2.6 Data annotation and labeling market, by application
- 16.2.2.7 Data annotation and labeling market, by vertical
- 16.2.2.8 Data annotation and labeling market, by region
- 16.3 SYNTHETIC DATA GENERATION MARKET
- 16.3.1 MARKET DEFINITION
- 16.3.2 MARKET OVERVIEW
- 16.3.2.1 Synthetic data generation market, by offering
- 16.3.2.2 Synthetic data generation market, by data type
- 16.3.2.3 Synthetic data generation market, by application
- 16.3.2.4 Synthetic data generation market, by vertical
- 16.3.2.5 Synthetic data generation market, by region
17 APPENDIX
- 17.1 DISCUSSION GUIDE
- 17.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
- 17.3 CUSTOMIZATION OPTIONS
- 17.4 RELATED REPORTS
- 17.5 AUTHOR DETAILS
