AI Training Dataset Market Rising Rapidly and Will Hit Revenue USD 9.89 BN by 2032

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The AI training dataset market revenue was valued at USD 2.09 billion in 2022 and it is expected to increase around USD 9.89 billion in 2032 with a CAGR of 16.82% from 2023 to 2032.

The AI training dataset market growth is driven by the fastest growth in AI and machine learning, increasing application of training datasets across industry verticals, growing demand for diverse datasets and Increasing investments.

Market Overview:

AI gives machines the ability to learn from experience, carry out human-like functions, and adapt to new input. They are taught to analyse vast amounts of data and identify patterns to carry out a specific job. Some datasets are needed to build these machines. To meet this need, there is an increasing demand for training databases for artificial intelligence.

Aartificial intelligence (AI) is used in machine learning, which enables systems to learn without expertise from being expressly programmed automatically. Machine learning focuses on creating software that can acquire and use data to make discoveries. Data used to build a machine learning model is referred to as AI training data. The training collection, training dataset, learning group, and ground truth data are also referred to as sources of AI training data in the data science community. These training datasets contain both the raw data and the expected results.

AI Training Dataset Market Report Scope:

Report Highlights:

• By type, the text segment is expected to dominate the market during the forecast period. The market is expanding because text datasets are widely used in the IT industry for a variety of automation processes, including speech recognition, text categorization, caption generation, and others. However, during the forecast period, the image/video segment is anticipated to expand at the highest CAGR. This is attributed to the continuous development from the key players that emphasises the release of new datasets with more applications. For instance, Google LLC, a global technology firm, announced the release of Google-Landmarks-v2, a new AI training dataset with millions of images and thousands of landmarks, in May 2020. Thus, this is expected to drive segment growth.
• By vertical, the IT segment is expected to dominate the market during the forecast period. Many companies are using machine learning to develop cutting-edge products and enhance the consumer experience. A sophisticated database is required for machine learning to work correctly. Additionally, quality data sets help IT companies with data analytics, virtual assistance, crowdsourcing, and computer vision.

Regional Insights:

North America is expected to dominate the market over the forecast period. To hasten the adoption of artificial intelligence technology in North America's developing industries, vendors are concentrating on launching new datasets. For instance, in September 2020, Waymo LLC, a subsidiary of Google LLC, published a new dataset for autonomous vehicles. This dataset contains sensor data that was gathered from camera sensors and LiDAR under a variety of driving circumstances, including the presence of people, cyclists, and other objects. Such advancements are influencing the market's usage of datasets and serving a sizable portion of the market.

On the other hand, the Asia Pacific is expected to grow at the highest CAGR during the projected period owing to the expanding presence of various players in the region. For instance, in July 2020, Microsoft introduced a dataset called Indoor Location Dataset to gather various data from structures situated in Chinese cities, such as the geomagnetic field and indoor wi-fi signature. These databases are meant to aid in the study and advancement of localization, indoor environments, and navigation. Thus, this is expected to drive market growth in the region.

Market Dynamics:

Drivers:

Increasing development of AI and ML

The market for artificial intelligence is expected to expand as a result of the emergence of big data, which requires the recording, storage, and analysis of massive quantities of data. The need to monitor and enhance big data-related computational models is more of a worry for end users. Their adoption of artificial intelligence solutions is accelerating due to this focus. It is expected that the adoption of artificial intelligence will significantly increase demand for AI training datasets because annotated data makes it easier to train machine learning and AI models in critical areas like speech and image recognition. 

AI is strengthened by annotating data with information that is necessary for making predictions while making alternatives. Many public and private organisations gather domain-specific data, which includes information from numerous applications like national intelligence, fraud detection, marketing, medical informatics, and cybersecurity. The labelling of unstructured and unsupervised data is made possible by data annotation, which constantly increases the accuracy of each data point.

Restraint:

The dearth of skilled professionals

Companies require a workforce with specialised skill sets to adopt and handle AI because it is a complicated system. A workforce running AI systems, for instance, should have expertise with technologies like deep learning, image recognition, machine learning, and cognitive computing. The integration of AI solutions with the current systems is a difficult job that requires large data processing that imitates the behaviour of the human brain. Thus, the dearth of skilled professionals is one of the major factors that restrain the market growth during the forecast period. 

Opportunities:

Growing adoption of training datasets in diverse industries

As a result of digital capturing devices, especially smartphone cameras, the volume of digital content such as images and videos is growing substantially. A significant quantity of Through a range of applications, websites, social networks, and other digital platforms, visual and digital information is being gathered and disseminated. Many companies have made use of this freely accessible online content with data annotation to offer customers more avant-garde and superior services. One of the most vital sources for a clinical study is now the unstructured text data gathered as a result of the growing use of electronic health record (EHR) systems. These factors should create an enormous amount of market growth opportunities over the forecast period.

Recent Developments:

• In January 2021, a partnership between the datasets supplier Vector Space AI and the search engine company Elasticsearch B.V. has been established. Users of the former business will be able to access AI datasets that were created in cooperation with the latter company. Datasets that will power AI, ML, and data engineering were introduced by Vectorspace AI.
• In December 2022, the recent announcement of a new partnership between Comet, a leading MLOps platform provider for machine learning (ML) teams in startups and enterprises, and Run:ai, a pioneer in compute orchestration for AI workloads, will speed up the workflows of ML practitioners and improve support for them at every stage of the ML lifecycle. Joint customers can access this best-in-class integrated solution that combines Comet's experiment management and model production monitoring with Run:ai's orchestration with ease, while new customers can fully benefit from this powerful integration to get the most out of their ML initiatives from early experimentation through production.
• In December 2022, a new, long-term strategic partnership between The London Stock Exchange Group (LSEG) and Microsoft has been formed to jointly develop new data and analytics products and services as well as to design LSEG's data infrastructure using the Microsoft Cloud. The collaboration will enhance LSEG's position as a top supplier of data and infrastructure for the financial markets and build on the company's successful integration of Refinitiv.

Major Key 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.

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