The global market for AI training datasets is projected to expand at a CAGR of 22.5% during the forecast period 2023 and 2031. AI is gaining prominence in numerous industrial applications, including manufacturing, IT, BFSI, retail and e-commerce, and healthcare. In addition to creating opportunities for new entrants, the rising demand for application-specific training data is generating new business opportunities. Artificial Intelligence (AI) is becoming increasingly important to big data, as the technology enables the extraction of high-level and complex abstractions through a hierarchical learning process, necessitating the mining and extraction of meaningful patterns from vast amounts of data.
AI enables machines to acquire knowledge through experience, perform human-like tasks, and adapt to new inputs. These machines are programmed to analyze massive amounts of data and identify patterns to complete a specific task. To train these machines, specific datasets are necessary. To meet this need, the demand for AI training datasets is increasing. The functionality of machines is entirely dependent on the provided dataset. Therefore, it becomes essential to provide high-quality training datasets. This high-quality dataset improves the performance of artificial intelligence. It also reduces the time needed to prepare data and improves the precision of predictions. Thus, market vendors are also focusing on acquiring companies that can help them improve data quality. For example, in March 2020, Appen Limited, a provider of specialized datasets, announced the acquisition of Figure Eight Inc., a machine-learning platform provider. Using automated tools, the second company transforms unlabeled data into high-quality information. This acquisition will assist the former company in accelerating the production of high-quality data sets. It will also contribute to the improvement of data quality.
Innovation and technological advancement in AI are accelerating the expansion of the market for AI training datasets. For instance, one of the most notable technological advancements is ChatGPT by Open AI, which can reduce the amount of time and resources needed to manually construct enormous datasets. ChatGPT can significantly reduce the time and resources required to generate a large dataset for NLP model training. As a large, unsupervised language model that was trained using GPT-3 technology, ChatGPT can generate human-like writing that can be used as training data for NLP applications. This enables it to rapidly and easily construct a vast and diverse dataset without requiring manual curation or the knowledge required to create a dataset that includes a wide variety of scenarios and situations.
Rapid Development of AI and Learning Machines
The emergence of big data, which necessitates the recording, storage, and analysis of voluminous amounts of data, is anticipated to stimulate the growth of the artificial intelligence market. End-users are more concerned with the need to monitor and improve big data-related computational models. This emphasis is accelerating their adoption of artificial intelligence solutions. Given that annotated data facilitates the training of AI models and machine learning systems in crucial domains such as speech recognition and image recognition, it is anticipated that the adoption of artificial intelligence will substantially increase demand for AI training datasets.
Annotating data with essential information for predicting future outcomes and making decisions strengthens AI. Numerous public and private organizations collect domain-specific data, including data from numerous applications such as national intelligence, fraud detection, marketing, medical informatics, and cybersecurity. Data annotation enables the labeling of unstructured and unsupervised data by continuously improving the precision of each data item.
Lack of Adoption of Technology in Developing Regions
In the Asia-Pacific region, substantial restrictions on the protection of personal information are anticipated to limit data collection. In Japan, for example, the Act on the Protection of Personal Information prohibits the transmission of sensitive personal data to unapproved entities or locations. The inaccuracy of data classification hinders the market's growth.
The main issue with data annotation tools is the precision of the output. Concerns about the output's quality, such as inaccurate data, should be minimized. In certain instances, manual labeling is performed incorrectly, and it can be time-consuming to locate these labels, thereby increasing the cost to the business. With the development of advanced algorithms, it is anticipated that the accuracy of automated AI data training dataset tools will improve, reducing the need for manual annotation and tool costs.
Increasing Training Dataset Applications in Diverse Industry Verticals
The amount of digital content in the form of photographs and videos has grown exponentially as a result of digital capturing devices, particularly smartphone cameras. Numerous applications, websites, social networks, and other digital channels are collecting and distributing a substantial amount of visual and digital information. Several businesses have used this freely available web content with data annotation to provide clients with more innovative and superior services. Unstructured text records collected as a result of the expanding use of Electronic Health Record (EHR) systems are now one of the most important resources for clinical research. Over the forecast period, these factors are anticipated to generate tremendous growth opportunities for the market.
Text Segment Dominates the Market by Type
In 2023, the text segment will account for a market share of 30%. This is due to the widespread use of text datasets in the IT industry for a variety of automation processes, including speech recognition, text classification, and caption generation, among others. Due to the availability of a wide variety of audio datasets, the audio segment is anticipated to have a moderate share. Among them are music datasets, speech datasets, a speech commands dataset, the Multimodal Emotion Lines Dataset (MELD), and environmental audio datasets.
The image/video segment is anticipated to experience the highest CAGR over the forecast period. This is because key players are focusing more on launching new datasets with a growing number of applications. In May 2020, for example, Google LLC, a multinational technology company, announced the launch of a new AI training dataset titled Google-Landmarks-v2 that contains millions of images and thousands of landmarks. Additionally, the business issued two challenges on Kaggle: landmark retrieval 2020 and landmark recognition 2020. These datasets were introduced for image retrieval and instance recognition, as well as for training more robust and effective systems.
The IT Segment remains the Dominant Vertical
In 2023, the IT market segment will hold a market share of 33%. The market is segmented by vertical into it, automotive, government, healthcare, BFSI, retail & e-commerce, and other segments. In therapy areas such as lifestyle and wellness management, diagnostics, virtual assistants, and wearables, AI in healthcare offers numerous opportunities. Aside from this, AI is utilized in voice-enabled symptom checkers and to enhance organizational workflow. All of these applications necessitate a large dataset to produce precise results. Consequently, the use of datasets will increase, resulting in a high CAGR over the forecast period.
Various technology companies on the market are utilizing machine learning to enhance the user experience and develop innovative products. Machine learning technology requires high-quality training data to ensure that ML algorithms are continuously optimized to be effective. In addition, high-quality datasets enable IT companies to improve a variety of solutions, including computer vision, crowdsourcing, data analytics, and virtual assistants. These factors contribute to the sector's extensive use of training datasets. In June 2021, for instance, Amazon released a large-scale dataset called Amazon Berkeley Objects to facilitate the development of new AI models for image-based shopping.
North America Remains as the Global Leader
In 2023, North America will account for 35% of the market share. North American market vendors are focusing on the release of new datasets to expedite the adoption of artificial intelligence technology in emerging industries. In September 2020, for instance, Waymo LLC, a subsidiary of Google LLC, released a new dataset for autonomous vehicles. This dataset contains sensor data collected from camera sensors and LiDAR under a variety of driving conditions, including cyclists, pedestrians, and signage. Such developments are driving the market's adoption of datasets, thereby catering to a substantial market share.
Asia-Pacific is the largest contributor to the global market for AI training datasets and is projected to expand at a CAGR of 21.5% over the forecast period. To modernize their businesses, businesses in developing nations such as India are significantly increasing their adoption of innovative technologies. In addition, several significant players are focusing on expanding their influence in Asia-Pacific. Microsoft, for instance, created the Indoor Location Dataset to collect various data from buildings in Chinese cities, such as the geomagnetic field and indoor Wi-Fi signature. These datasets contribute to the advancement and study of localization, indoor environments, and navigation. Furthermore, Microsoft and other major players are expanding their presence in this religion. It is anticipated that these factors will increase the use of datasets in the region and lead to a substantial increase over the forecast period. As business organizations in India strategize to transform their operations, the rate of adoption of emerging technologies is continuously increasing. In addition, numerous major players are concentrating on expanding their presence in Asia-Pacific. For example, in July 2020, Microsoft released a dataset titled Indoor Location Dataset to collect various information, such as the geomagnetic field, indoor signature of wi-fi, etc., in Chinese city buildings. These datasets are intended to facilitate navigation, indoor space, and localization research and development. In addition to Microsoft, numerous other prominent companies are expanding their presence in this region. These factors are anticipated to increase the usage of datasets in the region, resulting in a high growth rate over the forecast period. The European market is anticipated to experience moderate growth with a significant market share.
Market to Consolidate During the Forecast Period
The market is becoming increasingly consolidated as a result of strategic initiatives such as mergers, partnerships, and acquisitions. Key market participants are also concentrating on the release of new datasets. In January 2021, for instance, Vector Space AI, a provider of datasets, collaborated with Elasticsearch B.V., a search company. The former company will provide its users with AI datasets developed in collaboration with the latter. Vectorspace AI has introduced datasets that will be utilized for AI, ML, and data engineering. Similarly, Comet ML Inc. has developed a machine learning platform that aids data scientists in tracking, comparing, deriving meaning from, and optimizing experiments and models throughout the model's entire lifecycle, from training to production. For experiment tracking, data scientists can register code modifications, datasets, experimentation models, and history. The market for AI training datasets is dominated by companies such as Google LLC (Kaggle), Appen Limited, Cogito Tech LLC., Lionbridge Technologies, Inc., Amazon.com, Inc., Microsoft Corporation, Scale AI, Inc., Samasource, Inc., Alegion, Deep Vision Data and others.
Historical & Forecast Period
This study report represents analysis of each segment from 2022 to 2032 considering 2023 as the base year. Compounded Annual Growth Rate (CAGR) for each of the respective segments estimated for the forecast period of 2024 to 2032.
The current report comprises of quantitative market estimations for each micro market for every geographical region and qualitative market analysis such as micro and macro environment analysis, market trends, competitive intelligence, segment analysis, porters five force model, top winning strategies, top investment markets, emerging trends and technological analysis, case studies, strategic conclusions and recommendations and other key market insights.
Research Methodology
The complete research study was conducted in three phases, namely: secondary research, primary research, and expert panel review. key data point that enables the estimation of AI Training Dataset market are as follows:
Market forecast was performed through proprietary software that analyzes various qualitative and quantitative factors. Growth rate and CAGR were estimated through intensive secondary and primary research. Data triangulation across various data points provides accuracy across various analyzed market segments in the report. Application of both top down and bottom-up approach for validation of market estimation assures logical, methodical and mathematical consistency of the quantitative data.
| ATTRIBUTE | DETAILS |
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| Research Period | 2022-2032 |
| Base Year | 2023 |
| Forecast Period | 2024-2032 |
| Historical Year | 2022 |
| Unit | USD Million |
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Key questions answered in this report
