Global AI Training Dataset Market By Type (Image/Video, Text and Audio), By End User (IT & Telecom, Retail & E-commerce, Government, Healthcare, Automotive, and Others), By Regional Outlook, Industry Analysis Report and Forecast, 2021 - 2027
発行: KBV Research
ページ情報: 英文 188 Pages
List of Figures
The Global AI Training Dataset Market size is expected to reach $3.1 billion by 2027, rising at a market growth of 17.4% CAGR during the forecast period. Artificial Intelligence (AI) is considered as the broad branch of computer science that is associated with developing smart machines that can carry out tasks without the help of human intelligence. AI has gained a vital place in several industrial applications like IT, retail & e-commerce, healthcare, BFSI, and manufacturing. In addition, the rising demand for application-specific training data is offering lucrative opportunities for the new players. Artificial Intelligence has become important to big data because it enables to obtain the complex and high-level abstractions utilizing a hierarchical learning process and helps in obtaining meaningful patterns from large volume data through extraction and mining processes.
AI allows machines to perform tasks like a human by learning from experience and adjusting to the new inputs. Artificial Intelligence trains machines to process a huge volume of data and control patterns to complete the task given to them. Specific datasets are needed for the training of these machines. Thus, there is a huge demand for AI training datasets to fulfill this need in the market.
These machines perform tasks according to the dataset provided to them. Hence, it is necessary to offer superior-quality datasets to machines for better training. The superior-quality dataset helps in improving the performance level of artificial intelligence, resulting in decreasing the time taken to prepare data, and also helps in improving predictions precision. Therefore, the market players across the globe are aiming to acquire companies, which assist in improving the data quality.
COVID-19 Impact Analysis
The outbreak of the COVID-19 pandemic has encouraged developments in applications and technologies that are used in various sectors. Also, the pandemic has increased the adoption rate of AI in sectors like healthcare. The crisis has created a situation where all industries are facing challenges in running their business. To respond to this situation, AI-based tools and solutions have found their great deployment in all sectors. The key players in the market are focusing on shifting their business towards digitalization, due to which, there is a huge demand for AI solutions in the market.
Hence, these factors are accountable to have a positive effect on the AI training dataset market during the COVID-19 pandemic. In addition, to facilitate smooth operations of businesses during the pandemic, businessmen were compelled to deploy advanced analytics and other AI-based technologies. Moreover, businesses have become dependent on advanced technologies, which are anticipated to surge the growth of the market in the coming years. Further, several industries like healthcare, IT & automotive, and e-commerce are projected to fuel the deployment rate of the AI training dataset. Therefore, it can be estimated that the growth of the AI training dataset market will accelerate during the forecast period.
Market Growth Factors:
Several enhancements in the field of AI training dataset
A training dataset is a collection of information that is used to develop a machine learning model, through which the model creates and refines its rules. The quality of the training dataset has intense implications for the model's successive development, setting an ideal example for all future applications that may utilize the same training dataset.
Generation of large volume data and improvements in technology
The huge volume of data produced from several technologies like machine learning, big data, and artificial intelligence has increased the demand for AI training datasets. A large volume of unstructured and irrelevant data is produced by these technologies, thus, it is essential to train a machine learning model through precise and appropriate data.
Market Restraining Factor:
Lack of expertise
AI is a complicated system and for its adoption and management, companies need a workforce with special skill sets. For example, a workforce that is operating AI systems should have working experience with technologies like machine learning, machine intelligence, deep learning, image recognition, and cognitive computing. The incorporation of AI solutions with the present systems is a complex task that needs large data processing to replicate the human brain behavior.
Based on Type, the market is segmented into Image/Video, Text and Audio. The image or video type segment is anticipated to witness the highest growth rate over the forecast years. This surge in the growth of this segment is due to the increasing interest of key players of the markets towards the introduction of the latest datasets along associated with the growing number of applications.
End User Outlook
Based on End User, the market is segmented into IT & Telecom, Retail & E-commerce, Government, Healthcare, Automotive, and Others. Several technology companies across the market are utilizing machine learning solutions to offer a better user experience and introduce modern products. To be efficient, machine learning technology needs superior-quality training data to ensure that ML algorithms are continuously enhanced. Additionally, superior-quality datasets assist IT companies to improve several solutions like data analytics, computer vision, virtual assistants, crowdsourcing, and many others. These aspects are propelling the demand for great use of training datasets across the sector.
Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. There is a rapid surge in the deployment rate of the latest technologies by companies in emerging nations like India to bring improvement to their businesses. In addition, several key players are concentrating on increasing their existence in the Asia Pacific region. These determinants are projected to augment the utilization of dataset across the region and thus, are accounted to bolster the growth of the market during the forecast period.
The major strategies followed by the market participants are Product Launches. Based on the Analysis presented in the Cardinal matrix; Google, Inc. and Microsoft Corporation are the forerunners in the AI Training Dataset Market. Companies such as Amazon Web Services, Inc., Telus International, Scale AI Inc. are some of the key innovators in the market.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Google, LLC (Kaggle), Appen Limited, Cogito Tech LLC, Telus International (Telus Corporation), Amazon Web Services, Inc., Microsoft Corporation, Scale AI Inc., Sama Inc., Alegion, and Kinetic Vision, Inc. (Deep Vision Data).
Recent Strategies Deployed in AI Training Dataset Market
Partnerships, Collaborations and Agreements:
Jul-2021: Amazon came into a partnership with Hugging Face, an open-source provider of natural language processing (NLP) technologies. This partnership aimed to make it easier for enterprises to use State of Art Machine Learning models, and ship cutting-edge NLP features quicker. Following this partnership, Hugging Face would use Amazon Web Services as its Preferred Cloud Provider to provide services to its users.
Jun-2021: Scale AI formed a partnership with MIT Media Lab, a research laboratory at the Massachusetts Institute of Technology. This partnership aimed to implement ML in healthcare to help doctors in offering better care for patients.
May-2021: Microsoft came into partnership with Darktrace, a leading autonomous cyber security AI company. This partnership aimed to deliver unparalleled defense against sophisticated attacks, as companies are continuously shifting to the cloud.
Feb-2021: TELUS International extended its partnership with Google Cloud. Through this expansion, TELUS International would deliver deployment services for Google Cloud's Contact Center AI solution, enabling companies to modernize contact centers and deliver unique digital CX to end customers.
Aug-2020: Appen partnered with the World Economic Forum. Together, the entities aimed to develop and introduce standards and best practices for responsible training data whenever developing machine learning and AI applications. In addition, Appen would help in providing C-level decision-makers with main strategies for making and scaling AI programs by sourcing training data responsibly
Jul-2020: Microsoft entered into a partnership with SAS, an American multinational developer of analytics software. This partnership aimed to migrate SAS' analytical products and industry solutions onto Microsoft Azure. SAS' industry solutions and expertise would also add value to Microsoft's customers across financial services, health care, and many other industries.
Jun-2020: Microsoft came into a five-year partnership with PepsiCo, a leading global food and beverage company. This partnership aimed to support PepsiCo's operational objectives and aggressive innovation plans by using agile cloud capabilities along with offering Microsoft the opportunity to expand its partnership with a leading provider of consumer-packaged goods.
Acquisitions and Mergers
Aug-2021: Appen Limited entered into an agreement to acquire Quadrant, a global leader in mobile location data, Point-of-Interest data, and corresponding compliance services. This acquisition aimed to strengthen Appen's position in the market and also enable the company to provide high-quality data to companies that depend on geolocation for their business.
Jul-2021: TELUS International took over Lionbridge AI, a leading and global provider of scalable data annotation services for text, images, videos, and audio. This acquisition aimed to expand TELUS International's global service offerings and penetration into the fast-growing economy services market under their digital transformation strategy.
Jul-2021: Microsoft completed the acquisition of Nuance Communications, a speech recognition, and artificial intelligence company. This acquisition aimed to provide Microsoft with improved speech recognition and artificial intelligence technology and strengthen its presence in the healthcare sector.
Mar-2021: TELUS International took over Playment, a complete data labeling platform. Through this acquisition, Playment would enhance TELUS' deep domain expertise and uniquely position it to support customers in developing AI-powered solutions across verticals.
Product Launches and Expansions:
May-2021: Google Cloud unveiled Vertex AI, a managed machine learning platform. This platform would enable organizations to boost the deployment and management of AI models.
May-2021: Cogito expanded its capabilities in Pathology, Ophthalmology & Cardiology. The adoption of AI in healthcare requires expertise for accurately annotated data in healthcare.
Feb-2021: Appen Limited launched the latest off-the-shelf (OTS) datasets. These datasets are developed to make it simpler and quicker for companies to get the high-quality training data required to boost their artificial intelligence (AI) and machine learning (ML) projects.
Dec-2020: Amazon Web Services (AWS) introduced nine key updates for its cloud-based machine learning platform, SageMaker. These updates make it easier for developers to make end-to-end machine learning pipelines to create, build, explain, train, inspect, debug, monitor, and run custom machine learning models with more explainability, visibility, and automation at scale.
Oct-2020: Microsoft unveiled the public preview of a free app, Lobe. This app enables customers to train machine learning (ML) models without writing any code. The app demands to be shown examples of the way users want to learn, and the app automatically trains a custom machine learning model, which can be shipped in the users' app.
Aug-2020: Scale AI unveiled PandaSet: a new open-source dataset for training machine learning (ML) models for autonomous driving.
May-2020: Alegion introduced its next-generation video annotation solution. Alegion's video annotation solution is aimed at data science teams, which are developing object tracking algorithms that recognize and track individual objects of interest over time.
Market Segments covered in the Report:
By End User
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