AI Training Dataset Market
AI Training Dataset Market By Type (Text, Audio, Image/Video) By Vertical (IT, Government, Automotive, Healthcare, Retail & E-commerce, BFSI, Others) - Size, Share, Outlook, and Opportunity Analysis, 2023 2031
The global AI training dataset market size was reached at USD 2.10 billion in 2022 and it is expected to hit around USD 9.75 billion by 2031, growing at a CAGR of 17.82% from 2023 to 2031. The use of artificial intelligence technology is expanding. The need for technology is growing as organizations move towards automation. Technological advances have seen unprecedented advancements in marketing, logistics, transportation, healthcare, and many other industries. The acceptance of the technology has been fueled by the advantages of integrating it into various organizational operations that outweigh the costs. Due to the rapid adoption of AI technology, the demand for training datasets is growing tremendously. By creating multiple datasets that are used in a variety of settings to train the machine learning algorithm, many companies are increasing their market share and enabling the technology to be more flexible and accurate in its predictions.
Artificial intelligence programmes require a training dataset, commonly referred to as an artificial baseline, in order to teach models or machine learning algorithms how to make intelligent decisions. Because AI enables the extraction of complex, high-level abstract concepts through a hierarchical learning process, which necessitates data analysis and extraction, big data is becoming more and more dependent on AI. The provided dataset has a complete bearing on the machine's technique. So, providing excellent training datasets becomes essential. Due to the rapid adoption of AI technology, the demand for training datasets is growing tremendously. By creating multiple datasets that operate in a variety of settings to train the machine learning algorithm, many companies are increasing their market share and making the technology more adaptive and precise with its predictions.
COVID-19 Impact:
Applications and technology have advanced across several industries as a result of the COVID-19 pandemic. The rate at which AI is being deployed in industries like healthcare has also increased as a result of the pandemic. The crisis has made it difficult for firms in many sectors to operate. To address this issue, AI-based tools and solutions have been broadly embraced across all industries. The market's top competitors are focusing on making their businesses more digital, which is fueling a huge demand for AI solutions.
Future market expansion is expected to be accelerated by cutting-edge technology, which are also making businesses more dependent on them. Furthermore, it is projected that the deployment of the AI training dataset will pick up speed across a wide range of industries, including IT & automotive, e-commerce, and healthcare. So, it is possible to expect that throughout the anticipated time, the market for AI training datasets would grow more quickly.
AI Training Dataset Market REPORT SCOPE & SEGMENTATION
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Report Attribute |
Details |
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Estimated Market Value (2022) |
2.10 Bn |
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Projected Market Value (2031) |
9.75 Bn |
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Base Year |
2022 |
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Forecast Years |
2023 - 2031 |
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Scope of the Report |
Historical and Forecast Trends, Industry Drivers and Constraints, Historical and Forecast Market Analysis by Segment- By Type, By Vertical, By & Region |
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Segments Covered |
By Type, By Vertical, By & Region |
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Forecast Units |
Value (USD Billion), and Volume (Units) |
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Quantitative Units |
Revenue in USD million/billion and CAGR from 2023 to 2031 |
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Regions Covered |
North America, Europe, Asia Pacific, Latin America, and Middle East & Africa, and Rest of World |
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Countries Covered |
U.S., Canada, Mexico, U.K., Germany, France, Italy, Spain, China, India, Japan, South Korea, Brazil, Argentina, GCC Countries, and South Africa, among others |
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Report Coverage |
Market growth drivers, restraints, opportunities, Porter’s five forces analysis, PEST analysis, value chain analysis, regulatory landscape, market attractiveness analysis by segments and region, company market share analysis, and COVID-19 impact analysis. |
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Delivery Format |
Delivered as an attached PDF and Excel through email, according to the purchase option. |
Segments Analysis:
Type Analysis
The global market for AI training datasets is segmented into text, audio, image, and video forms. The text market outperformed the market's predictions for AI training datasets in 2022 with a market share of 30.80%. Text datasets are frequently utilized in the IT sector for a variety of automated tasks, such as text classification, caption creation, and speech recognition.
The audio segment is anticipated to have a good market share because to the wide variety of audio datasets that are currently available. Speech and music datasets, speech commands, environmental audio datasets, the Multimodal Emotion Lines Dataset, and many others are examples.
Vertical Analysis
Based on vertical, the market for AI training datasets is segmented into the automotive, healthcare, information technology, government, and other sectors. With a market share of over 34% in 2022, the IT segment led the sector. Additionally, the use of AI in healthcare creates numerous therapeutic possibilities, including the use of virtual assistants, wearable technology, wellness and lifestyle management, and diagnostics.
Other applications of AI include voice-activated symptom checks and enhanced organizational processes. To deliver correct results, these apps need a sizable training dataset. The growth of datasets will cause a high CAGR during the projection period.
Regional Analysis:
The North American, Asia Pacific, Middle Eastern, European, Latin American, and African regions make up the majority of the global market for AI training datasets. North America is predicted to hold over 40% of the global market for AI Training Datasets in 2022. Market players are concentrating on introducing fresh datasets in order to hasten the adoption of artificial intelligence technology in developing North American regions.
For instance, in September 2020, Waymo LLC, a Google LLC subsidiary, produced a unique dataset for driverless vehicles. Several driving scenarios, including those including bikes, signs, pedestrians, and other road users, were used to collect the data for this dataset utilising camera sensors and LiDAR.
Competitive Landscape
The competitive landscape of the AI training dataset market is dynamic and rapidly evolving, with several key players competing for market share. These players are mainly focused on improving the quality and quantity of training data, expanding their customer base, and developing new and innovative solutions to meet the growing demand for AI training datasets.
Key Market Players:
- Google, LLC (Kaggle)
- Deep Vision Data
- Cogito Tech LLC
- Appen Limited
- Samasource Inc.
- Lionbridge Technologies, Inc.
- Microsoft Corporation
- Alegion
- Amazon Web Services, Inc.
- Scale AI Inc.
Key Benefits of the Report
- This study presents an analytical depiction of the AI Training Dataset industry along with current trends and future estimations to determine the imminent investment pockets.
- The report presents information related to market key drivers, restraints, and opportunities, along with detailed analysis of the AI Training Dataset Market share.
- The current market is quantitatively analyzed to highlight the AI Training Dataset Market's growth scenario.
- Porter's five forces research demonstrates the market power of suppliers and buyers.
- The report provides a detailed AI Training Dataset market analysis based on competitive intensity and how the competition will take shape in the coming years.
AI Training Dataset Market Report Segmentation
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ATTRIBUTE |
DETAILS |
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By Type |
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By Vertical |
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By Geography |
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Customization Scope |
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Pricing |
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The Report Answers Questions Such As:
- What is the potential opportunity for the AI Training Dataset Market
- What are the major drivers, restraints, and opportunities of the AI Training Dataset Market
- What is the market share of the leading segments and sub-segments of the AI Training Dataset Market in the forecast period (2023-2031)
- How is each segment of the AI Training Dataset Market expected to grow during the forecast period
- What is the expected revenue to be generated by each of the segments by the end of 2031
- What are the key development strategies implemented by the key players to stand out in this AI Training Dataset Market
- What is the preferred business model used for building AI Training Dataset Market
- Which area of application is expected to be the highest revenue generator in the AI Training Dataset Market during the forecast period
- Which end-user segment is expected to be the highest revenue generator in this industry during the forecast period
Research Methodology
Our research methodology has always been the key differentiating reason which sets us apart in comparison from the competing organizations in the industry. Our organization believes in consistency along with quality and establishing a new level with every new report we generate; our methods are acclaimed and the data/information inside the report is coveted. Our research methodology involves a combination of primary and secondary research methods. Data procurement is one of the most extensive stages in our research process. Our organization helps in assisting the clients to find the opportunities by examining the market across the globe coupled with providing economic statistics for each and every region. The reports generated and published are based on primary & secondary research. In secondary research, we gather data for global Market through white papers, case studies, blogs, reference customers, news, articles, press releases, white papers, and research studies. We also have our paid data applications which includes hoovers, Bloomberg business week, Avention, and others.
Data Collection
Data collection is the process of gathering, measuring, and analyzing accurate and relevant data from a variety of sources to analyze market and forecast trends. Raw market data is obtained on a broad front. Data is continuously extracted and filtered to ensure only validated and authenticated sources are considered. Data is mined from a varied host of sources including secondary and primary sources.
Primary Research
After the secondary research process, we initiate the primary research phase in which we interact with companies operating within the market space. We interact with related industries to understand the factors that can drive or hamper a market. Exhaustive primary interviews are conducted. Various sources from both the supply and demand sides are interviewed to obtain qualitative and quantitative information for a report which includes suppliers, product providers, domain experts, CEOs, vice presidents, marketing & sales directors, Type & innovation directors, and related key executives from various key companies to ensure a holistic and unbiased picture of the market.
Secondary Research
A secondary research process is conducted to identify and collect information useful for the extensive, technical, market-oriented, and comprehensive study of the market. Secondary sources include published market studies, competitive information, white papers, analyst reports, government agencies, industry and trade associations, media sources, chambers of commerce, newsletters, trade publications, magazines, Bloomberg BusinessWeek, Factiva, D&B, annual reports, company house documents, investor presentations, articles, journals, blogs, and SEC filings of companies, newspapers, and so on. We have assigned weights to these parameters and quantified their market impacts using the weighted average analysis to derive the expected market growth rate.
Top-Down Approach & Bottom-Up Approach
In the top – down approach, the Global Batteries for Solar Energy Storage Market was further divided into various segments on the basis of the percentage share of each segment. This approach helped in arriving at the market size of each segment globally. The segments market size was further broken down in the regional market size of each segment and sub-segments. The sub-segments were further broken down to country level market. The market size arrived using this approach was then crosschecked with the market size arrived by using bottom-up approach.
In the bottom-up approach, we arrived at the country market size by identifying the revenues and market shares of the key market players. The country market sizes then were added up to arrive at regional market size of the decorated apparel, which eventually added up to arrive at global market size.
This is one of the most reliable methods as the information is directly obtained from the key players in the market and is based on the primary interviews from the key opinion leaders associated with the firms considered in the research. Furthermore, the data obtained from the company sources and the primary respondents was validated through secondary sources including government publications and Bloomberg.
Market Analysis & size Estimation
Post the data mining stage, we gather our findings and analyze them, filtering out relevant insights. These are evaluated across research teams and industry experts. All this data is collected and evaluated by our analysts. The key players in the industry or markets are identified through extensive primary and secondary research. All percentage share splits, and breakdowns have been determined using secondary sources and verified through primary sources. The market size, in terms of value and volume, is determined through primary and secondary research processes, and forecasting models including the time series model, econometric model, judgmental forecasting model, the Delphi method, among Flywheel Energy Storage. Gathered information for market analysis, competitive landscape, growth trends, product development, and pricing trends is fed into the model and analyzed simultaneously.
Quality Checking & Final Review
The analysis done by the research team is further reviewed to check for the accuracy of the data provided to ensure the clients’ requirements. This approach provides essential checks and balances which facilitate the production of quality data. This Type of revision was done in two phases for the authenticity of the data and negligible errors in the report. After quality checking, the report is reviewed to look after the presentation, Type and to recheck if all the requirements of the clients were addressed.
