In a number of industrial uses, including manufacturing, IT, BFSI, retail & e-commerce, and healthcare, AI is indeed extremely relevant. Opportunities for new candidates are also emerging due to the rising demand for application-specific training data. Big data is increasingly dependent on artificial intelligence (AI), as the technology enables the extraction of highly complicated abstractions through a hierarchical learning process, necessitating the mining and extraction of meaningful patterns from vast amounts of data. Furthermore, AI gives machines the ability to execute human-like tasks, learn from experience, and adapt to new inputs. These machines are taught to analyze vast amounts of data and find patterns in order to carry out a specific job. Specific datasets are needed to teach these machines. To meet this need, there is a rising demand for AI training databases.
Furthermore, Factors such as machine learning and Intelligence are expanding quickly, and the production of large amounts of data and technological advancements primarily drive the growth of the AI training dataset market. However, poor expertise of technology in developing areas hampers market growth to some extent. Moreover, widening functionality of training data sets in multiple business verticals is expected to provide lucrative opportunities for the market growth during the forecast period.
Depending on the type, the text segment dominated the AI training dataset market share in 2021 and is expected to continue this dominance during the forecast period, owing to train AI text generators so they can learn the structure and style of writing. It may be used to automatically compose articles, blog posts, product descriptions, and other sorts of content after they have mastered the ability to produce writing that sounds human. However, the image/video segment is expected to witness the highest growth in the upcoming years, owing to creates its own parameters for how to represent or model those patterns in order to forecast fresh data (such to identify objects in a video) or create entirely new content that closely resembles the training data (such as synthesize images from a collection of paintings).
Region wise, the AI training dataset market was dominated by North America in 2021 and is expected to retain its position during the forecast period, owing to industries moving towards automation, there is a higher demand for AI and machine learning tools. The demand for analytical solutions to acquire the best visualization and strategy developments is being driven by the rapid digitalization of company. However, Asia-Pacific is expected to witness the highest growth in the upcoming years, owing to the widespread release of new datasets to speed up the usage of artificial intelligence technology in developing sectors. Emerging technologies are being quickly embraced by businesses in developing nations like India in order to modernize their operations. Other important players are also focusing their efforts in the area.
The global AI training dataset industry is dominated by key players such as Google LLC, Amazon Web Services Inc., Microsoft Corporation, SCALE AI, INC., APPEN LIMITED, Cogito Tech LLC, Lionbridge Technologies, Inc., Alegion, Deep Vision Data, Samasource Inc. These players have adopted various strategies to increase their market penetration and strengthen their position in AI training dataset industry.
Since big data requires the recording, storing, and analysis of a sizable quantity of data, its emergence is expected to fuel the growth of the artificial intelligence market. End users are more concerned with the necessity of maintaining and improving the big data-related analytical models. They are adopting artificial intelligence solutions more rapidly as a result of this focus. The adoption of artificial intelligence is anticipated to significantly increase demand for AI training datasets because annotated data stimulates training AI models and machine learning systems in crucial areas like speech recognition and image identification. By clearly providing the data needed for decision-making and future prediction, data annotation strengthens AI. Many public and private organizations gather domain-specific data, including data from numerous applications like national espionage, fraud detection, marketing, medical informatics, and cyber security. Data annotation makes it possible to label such unstructured and unsupervised data by constantly increasing the accuracy of each individual piece of information.
Furthermore, Factors such as machine learning and Intelligence are expanding quickly, and the production of large amounts of data and technological advancements primarily drive the growth of the AI training dataset market. However, poor expertise of technology in developing areas hampers market growth to some extent. Moreover, widening functionality of training data sets in multiple business verticals is expected to provide lucrative opportunities for the market growth during the forecast period.
Depending on the type, the text segment dominated the AI training dataset market share in 2021 and is expected to continue this dominance during the forecast period, owing to train AI text generators so they can learn the structure and style of writing. It may be used to automatically compose articles, blog posts, product descriptions, and other sorts of content after they have mastered the ability to produce writing that sounds human. However, the image/video segment is expected to witness the highest growth in the upcoming years, owing to creates its own parameters for how to represent or model those patterns in order to forecast fresh data (such to identify objects in a video) or create entirely new content that closely resembles the training data (such as synthesize images from a collection of paintings).
Region wise, the AI training dataset market was dominated by North America in 2021 and is expected to retain its position during the forecast period, owing to industries moving towards automation, there is a higher demand for AI and machine learning tools. The demand for analytical solutions to acquire the best visualization and strategy developments is being driven by the rapid digitalization of company. However, Asia-Pacific is expected to witness the highest growth in the upcoming years, owing to the widespread release of new datasets to speed up the usage of artificial intelligence technology in developing sectors. Emerging technologies are being quickly embraced by businesses in developing nations like India in order to modernize their operations. Other important players are also focusing their efforts in the area.
Segment review
The AI training dataset market is segmented on the basis of type, industry vertical and region. On the basis of type, it is segregated into Text, Audio, and Image/Video. On the basis industry vertical, it is classified into IT and telecom, BFSI, automotive, healthcare, government and defense, retail, and others. On the basis of region, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.The global AI training dataset industry is dominated by key players such as Google LLC, Amazon Web Services Inc., Microsoft Corporation, SCALE AI, INC., APPEN LIMITED, Cogito Tech LLC, Lionbridge Technologies, Inc., Alegion, Deep Vision Data, Samasource Inc. These players have adopted various strategies to increase their market penetration and strengthen their position in AI training dataset industry.
Top impacting factors
Quick expansion of Machine learning (ML) and Artificial Intelligence (AI)Since big data requires the recording, storing, and analysis of a sizable quantity of data, its emergence is expected to fuel the growth of the artificial intelligence market. End users are more concerned with the necessity of maintaining and improving the big data-related analytical models. They are adopting artificial intelligence solutions more rapidly as a result of this focus. The adoption of artificial intelligence is anticipated to significantly increase demand for AI training datasets because annotated data stimulates training AI models and machine learning systems in crucial areas like speech recognition and image identification. By clearly providing the data needed for decision-making and future prediction, data annotation strengthens AI. Many public and private organizations gather domain-specific data, including data from numerous applications like national espionage, fraud detection, marketing, medical informatics, and cyber security. Data annotation makes it possible to label such unstructured and unsupervised data by constantly increasing the accuracy of each individual piece of information.
Production of large amounts of data and technological advancements
Big data is used by businesses to enhance operations, deliver better customer service, develop individualized marketing campaigns, and carry out other tasks that can eventually boost sales and profits. Because they can act more quickly and with greater knowledge, businesses that use it successfully may have a competitive advantage over those that don't. Moreover, big data, for instance, offers insightful information about consumers that businesses can use to improve their marketing, advertising, and promotions and boost customer engagement and conversion rates. Furthermore, businesses can become more responsive to customer desires and requirements by analyzing historical and real-time data to evaluate the changing preferences of consumers or corporate buyers. Therefore, the demand for AI training datasets has grown as a result of the enormous volume of data generated by numerous technologies such as machine learning, big data, and artificial intelligence. These technologies generate a significant amount of unstructured and irrelevant data; as a result, it is crucial to feed a machine learning model accurate and pertinent data when training it.Key Benefits For Stakeholders
- This report provides a quantitative analysis of the market segments, current trends, estimations, and dynamics of the ai training dataset market analysis from 2021 to 2031 to identify the prevailing ai training dataset market opportunities.
- The market research is offered along with information related to key drivers, restraints, and opportunities.
- Porter's five forces analysis highlights the potency of buyers and suppliers to enable stakeholders make profit-oriented business decisions and strengthen their supplier-buyer network.
- In-depth analysis of the ai training dataset market segmentation assists to determine the prevailing market opportunities.
- Major countries in each region are mapped according to their revenue contribution to the global market.
- Market player positioning facilitates benchmarking and provides a clear understanding of the present position of the market players.
- The report includes the analysis of the regional as well as global ai training dataset market trends, key players, market segments, application areas, and market growth strategies.
Key Market Segments
By Type
- Text
- Audio
- Image/Video
By End User
- IT and Telecom
- BFSI
- Automotive
- Healthcare
- Government and Defense
- Retail
- Others
By Region
- North America
- U.S.
- Canada
- Europe
- UK
- Germany
- France
- Italy
- Spain
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- Australia
- South Korea
- Rest of Asia-Pacific
- LAMEA
- Latin America
- Middle East
- Africa
Key Market Players
- Google LLC
- Amazon Web Services Inc.
- Microsoft Corporation
- SCALE AI, INC.
- APPEN LIMITED
- Cogito Tech LLC
- Lionbridge Technologies, Inc
- Alegion
- Deep Vision Data
- Samasource Inc.
Table of Contents
CHAPTER 1: INTRODUCTION
CHAPTER 2: EXECUTIVE SUMMARY
CHAPTER 3: MARKET OVERVIEW
CHAPTER 4: AI TRAINING DATASET MARKET, BY TYPE
CHAPTER 5: AI TRAINING DATASET MARKET, BY END USER
CHAPTER 6: AI TRAINING DATASET MARKET, BY REGION
CHAPTER 7: COMPETITIVE LANDSCAPE
CHAPTER 8: COMPANY PROFILES
List of Tables
List of Figures
Companies Mentioned
- Google LLC
- Amazon Web Services Inc.
- Microsoft Corporation
- SCALE AI, INC.
- APPEN LIMITED
- Cogito Tech LLC
- Lionbridge Technologies, Inc
- Alegion
- Deep Vision Data
- Samasource Inc.
Methodology
The analyst offers exhaustive research and analysis based on a wide variety of factual inputs, which largely include interviews with industry participants, reliable statistics, and regional intelligence. The in-house industry experts play an instrumental role in designing analytic tools and models, tailored to the requirements of a particular industry segment. The primary research efforts include reaching out participants through mail, tele-conversations, referrals, professional networks, and face-to-face interactions.
They are also in professional corporate relations with various companies that allow them greater flexibility for reaching out to industry participants and commentators for interviews and discussions.
They also refer to a broad array of industry sources for their secondary research, which typically include; however, not limited to:
- Company SEC filings, annual reports, company websites, broker & financial reports, and investor presentations for competitive scenario and shape of the industry
- Scientific and technical writings for product information and related preemptions
- Regional government and statistical databases for macro analysis
- Authentic news articles and other related releases for market evaluation
- Internal and external proprietary databases, key market indicators, and relevant press releases for market estimates and forecast
Furthermore, the accuracy of the data will be analyzed and validated by conducting additional primaries with various industry experts and KOLs. They also provide robust post-sales support to clients.
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