AI Training Dataset Market Size, Growth, Trends | Report 2023-2032
The global AI training dataset market was surpassed at USD 1.77 billion in 2022 and is expected to hit around USD 13.07 billion by 2032, growing at a CAGR of 22.13% from 2023 to 2032.
Key Pointers
Report Scope of the AI Training Dataset Market
| Report Coverage | Details |
| Market Size in 2022 | USD 1.77 billion |
| Revenue Forecast by 2032 | USD 13.07 billion |
| Growth rate from 2023 to 2032 | CAGR of 22.13% |
| Base Year | 2022 |
| Forecast Period | 2023 to 2032 |
| Regions Covered | North America, Europe, Asia Pacific, Latin America, Middle East & Africa |
| Companies Covered | Google, LLC (Kaggle); Appen Limited; Cogito Tech LLC; Lionbridge Technologies, Inc.; Amazon Web Services, Inc.; Microsoft Corporation; Scale AI; Inc.; Samasource Inc.; Alegion; Deep Vision Data. |
AI is gaining significant importance in various industrial applications such as manufacturing, IT, BFSI, retail & e-commerce, and healthcare. The growing demand for application-specific training data is also opening opportunities for new entrants. Artificial Intelligence (AI) is becoming vital to big data as the technology allows the extraction of high-level and complex abstractions using a hierarchical learning process leading to the need for mining and extracting meaningful patterns from voluminous data.
AI enables machines to learn from experience, perform human-like tasks, and adjust to new inputs. These machines are trained to process massive data and determine patterns to accomplish a specific task. In order to train these machines, certain datasets are required. Hence, the demand for AI training datasets is increasing to cater to this requirement.
The working of machines entirely depends on the dataset provided. Thus, it becomes essential to provide high-quality datasets for training. This high-quality dataset enhances the performance of AI. It also helps in reducing the time required to prepare data and increases the accuracy of predictions. Thus, vendors in the market are also focusing on acquiring companies that can help them to enhance the quality of data. For instance, In March 2020, Appen Limited, a specialized dataset provider, announced the acquisition of Figure Eight Inc., a provider of the machine learning platform. The latter company creates high-quality data by transforming unlabeled data with the help of automated tools. This acquisition will help the former company to increase the creation speed of a high-quality dataset. It will also help in enhancing the quality of data.
Technological advancement and Innovation in AI is augmenting the market growth of AI training dataset. For instance, one of the prominent technological innovations is ChatGPT by Open AI, which has the ability to reduce the time and resources required to manually construct huge datasets. ChatGPT can significantly reduce the time and resources needed to create a large dataset for training an NLP model. ChatGPT can produce human-like writing that can be utilized as training data for NLP applications because it is a sizable, unsupervised language model that was trained using GPT-3 technology. This makes it possible for it to rapidly and simply construct a vast and diverse dataset without the need for manual curation or the knowledge needed to create a dataset that includes a wide range of scenarios and situations.
Regional Insights
North America caters to a market share of 37.2% in 2022. Vendors in the North American market are focusing on releasing new datasets to accelerate the adoption of artificial intelligence technology in emerging sectors in North America. For instance, Waymo LLC, a Google LLC company, released a new dataset for autonomous vehicles in September 2020. This dataset comprises sensor data that has been collected from camera sensors and LiDAR under various driving conditions such as cyclists, pedestrians, signage, and others. Such developments are driving the adoption of datasets in the market, thereby catering to a high share of the market. Also, various key players are focusing on expanding their presence in the Asia Pacific.
AI Training Dataset Market Segmentations:
| By Type | By Vertical |
|
Text Image/Video Audio |
IT Automotive Government Healthcare BFSI Retail & E-commerce Others |
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on AI Training Dataset Market
5.1. COVID-19 Landscape: AI Training Dataset Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global AI Training Dataset Market, By Type
8.1. AI Training Dataset Market, by Type, 2023-2032
8.1.1. Text
8.1.1.1. Market Revenue and Forecast (2020-2032)
8.1.2. Image/Video
8.1.2.1. Market Revenue and Forecast (2020-2032)
8.1.3. Audio
8.1.3.1. Market Revenue and Forecast (2020-2032)
Chapter 9. Global AI Training Dataset Market, By Vertical
9.1. AI Training Dataset Market, by Vertical, 2023-2032
9.1.1. IT
9.1.1.1. Market Revenue and Forecast (2020-2032)
9.1.2. Automotive
9.1.2.1. Market Revenue and Forecast (2020-2032)
9.1.3. Government
9.1.3.1. Market Revenue and Forecast (2020-2032)
9.1.4. Healthcare
9.1.4.1. Market Revenue and Forecast (2020-2032)
9.1.5. BFSI
9.1.5.1. Market Revenue and Forecast (2020-2032)
9.1.6. Retail & E-commerce
9.1.6.1. Market Revenue and Forecast (2020-2032)
9.1.7. Others
9.1.7.1. Market Revenue and Forecast (2020-2032)
Chapter 10. Global AI Training Dataset Market, Regional Estimates and Trend Forecast
10.1. North America
10.1.1. Market Revenue and Forecast, by Type (2020-2032)
10.1.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.1.3. U.S.
10.1.3.1. Market Revenue and Forecast, by Type (2020-2032)
10.1.3.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.1.4. Rest of North America
10.1.4.1. Market Revenue and Forecast, by Type (2020-2032)
10.1.4.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.2. Europe
10.2.1. Market Revenue and Forecast, by Type (2020-2032)
10.2.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.2.3. UK
10.2.3.1. Market Revenue and Forecast, by Type (2020-2032)
10.2.3.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.2.4. Germany
10.2.4.1. Market Revenue and Forecast, by Type (2020-2032)
10.2.4.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.2.5. France
10.2.5.1. Market Revenue and Forecast, by Type (2020-2032)
10.2.5.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.2.6. Rest of Europe
10.2.6.1. Market Revenue and Forecast, by Type (2020-2032)
10.2.6.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.3. APAC
10.3.1. Market Revenue and Forecast, by Type (2020-2032)
10.3.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.3.3. India
10.3.3.1. Market Revenue and Forecast, by Type (2020-2032)
10.3.3.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.3.4. China
10.3.4.1. Market Revenue and Forecast, by Type (2020-2032)
10.3.4.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.3.5. Japan
10.3.5.1. Market Revenue and Forecast, by Type (2020-2032)
10.3.5.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.3.6. Rest of APAC
10.3.6.1. Market Revenue and Forecast, by Type (2020-2032)
10.3.6.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.4. MEA
10.4.1. Market Revenue and Forecast, by Type (2020-2032)
10.4.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.4.3. GCC
10.4.3.1. Market Revenue and Forecast, by Type (2020-2032)
10.4.3.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.4.4. North Africa
10.4.4.1. Market Revenue and Forecast, by Type (2020-2032)
10.4.4.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.4.5. South Africa
10.4.5.1. Market Revenue and Forecast, by Type (2020-2032)
10.4.5.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.4.6. Rest of MEA
10.4.6.1. Market Revenue and Forecast, by Type (2020-2032)
10.4.6.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.5. Latin America
10.5.1. Market Revenue and Forecast, by Type (2020-2032)
10.5.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.5.3. Brazil
10.5.3.1. Market Revenue and Forecast, by Type (2020-2032)
10.5.3.2. Market Revenue and Forecast, by Vertical (2020-2032)
10.5.4. Rest of LATAM
10.5.4.1. Market Revenue and Forecast, by Type (2020-2032)
10.5.4.2. Market Revenue and Forecast, by Vertical (2020-2032)
Chapter 11. Company Profiles
11.1. Google, LLC (Kaggle)
11.1.1. Company Overview
11.1.2. Product Offerings
11.1.3. Financial Performance
11.1.4. Recent Initiatives
11.2. Appen Limited
11.2.1. Company Overview
11.2.2. Product Offerings
11.2.3. Financial Performance
11.2.4. Recent Initiatives
11.3. Cogito Tech LLC
11.3.1. Company Overview
11.3.2. Product Offerings
11.3.3. Financial Performance
11.3.4. Recent Initiatives
11.4. Lionbridge Technologies, Inc.
11.4.1. Company Overview
11.4.2. Product Offerings
11.4.3. Financial Performance
11.4.4. LTE Scientific
11.5. Amazon Web Services, Inc.
11.5.1. Company Overview
11.5.2. Product Offerings
11.5.3. Financial Performance
11.5.4. Recent Initiatives
11.6. Microsoft Corporation
11.6.1. Company Overview
11.6.2. Product Offerings
11.6.3. Financial Performance
11.6.4. Recent Initiatives
11.7. Scale AI; Inc.
11.7.1. Company Overview
11.7.2. Product Offerings
11.7.3. Financial Performance
11.7.4. Recent Initiatives
11.8. Samasource Inc.
11.8.1. Company Overview
11.8.2. Product Offerings
11.8.3. Financial Performance
11.8.4. Recent Initiatives
11.9. Alegion
11.9.1. Company Overview
11.9.2. Product Offerings
11.9.3. Financial Performance
11.9.4. Recent Initiatives
11.10. Deep Vision Data.
11.10.1. Company Overview
11.10.2. Product Offerings
11.10.3. Financial Performance
11.10.4. Recent Initiatives
Chapter 12. Research Methodology
12.1. Primary Research
12.2. Secondary Research
12.3. Assumptions
Chapter 13. Appendix
13.1. About Us
13.2. Glossary of Terms
Cross-segment Market Size and Analysis for
Mentioned Segments
Additional Company Profiles (Upto 5 With No Cost)
Additional Countries (Apart From Mentioned Countries)
Country/Region-specific Report
Go To Market Strategy
Region Specific Market DynamicsRegion Level Market Share Import Export AnalysisProduction AnalysisOthers