The Generative AI Market size was valued at USD 43.87 USD Billion in 2023 and is projected to reach USD 453.28 USD Billion by 2032, exhibiting a CAGR of 39.6 % during the forecast period. The market's expansion is driven by the increasing adoption of AI in various industries, the growing demand for personalized experiences, and the advancement of machine learning and deep learning technologies. Generative AI is a form of AI technology that come with the capability to generate content in several of forms such us that include text, images, audio data, and artificial data. In the latest trend of the use of generative AI, fingertip friendly interfaces that allow for the creation of top-quality text design, and videos in a brief time of only seconds have been the leading cause of the hype around it. The AI technology called Generative AI employs a variety of techniques that its development is still being improved. Fundamentally, AI foundation models are based on training on a wide spate of unlabelled data that can be used for many tasks; working primarily on specific areas where additional fine-tuning finds its place. Over-simplifying the process, huge amounts of maths and computer power get used to develop AI models. Nevertheless, at its core, it is the predictions amplified. Generative AI relies on deep learning models – sophisticated machine learning models that work as neural networks and learn and take decisions just the human minds do. Such models are based on the detection and emission of codes of complex relationships or patterns in huge information volumes and that data is used to respond to users' original speech requests or questions with native language replies or new content. Recent developments include: June 2023: Salesforce launched two generative artificial intelligence (AI) products for commerce experience and customized consumers –Commerce GPT and Marketing GPT. The Marketing GPT model leverages data from Salesforce's real-time data cloud platform to generate more innovative audience segments, personalized emails, and marketing strategies., June 2023: Accenture and Microsoft are teaming up to help companies primarily transform their businesses by harnessing the power of generative AI accelerated by the cloud. It helps customers find the right way to build and extend technology in their business responsibly., May 2023: SAP SE partnered with Microsoft to help customers solve their fundamental business challenges with the latest enterprise-ready innovations. This integration will enable new experiences to improve how businesses attract, retain and qualify their employees. , April 2023: Amazon Web Services, Inc. launched a global generative AI accelerator for startups. The company’s Generative AI Accelerator offers access to impactful AI tools and models, machine learning stack optimization, customized go-to-market strategies, and more., March 2023: Adobe and NVIDIA have partnered to join the growth of generative AI and additional advanced creative workflows. Adobe and NVIDIA will innovate advanced AI models with new generations aiming at tight integration into the applications that significant developers and marketers use. . Key drivers for this market are: Growing Necessity to Create a Virtual World in the Metaverse to Drive the Market. Potential restraints include: Risks Related to Data Breaches and Sensitive Information to Hinder Market Growth . Notable trends are: Rising Awareness about Conversational AI to Transform the Market Outlook .

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We offer web activity data of users that are browsing popular websites around the world. This data can be used to analyze web behavior across the web and build highly accurate audience segments based on web activity for targeting ads based on interest categories and search/browsing intent.

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Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

AI Data Resource Service Market Outlook

The global AI Data Resource Service market size was valued at approximately $5.2 billion in 2023 and is projected to reach around $21.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 17.1% during the forecast period. This significant growth can be attributed to various factors including the exponential increase in data generation, advancements in artificial intelligence technologies, and the rising need for efficient data management solutions across different sectors.

One of the primary growth factors for the AI Data Resource Service market is the rapid expansion of data generation from various sources such as Internet of Things (IoT) devices, social media, and enterprise data systems. Organizations are increasingly seeking advanced solutions to manage, analyze, and extract valuable insights from this vast amount of data. AI data resource services offer enhanced capabilities to handle and process data efficiently, thereby driving their adoption across different industries.

Another important factor contributing to the market's growth is the continuous advancements in AI technology. Progressive developments in machine learning algorithms, natural language processing, and predictive analytics are enhancing the capabilities of AI data resource services. These advancements enable organizations to gain deeper insights, automate complex processes, and improve decision-making, thereby adding significant value to their operations and propelling market growth.

The demand for AI data resource services is further fueled by the increasing need for real-time data analytics and the growing emphasis on data-driven decision-making. In today’s competitive business environment, organizations are striving to leverage data analytics to gain a competitive edge. AI data resource services provide the necessary tools and frameworks to process data in real-time, enabling faster and more accurate business insights. This trend is particularly prevalent in sectors such as finance, healthcare, and retail, where timely and precise data analysis is critical.

From a regional perspective, North America currently holds the largest market share in the AI data resource service market. The region's dominance can be attributed to the presence of major technology companies, a robust IT infrastructure, and significant investments in AI research and development. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The rapid digitization of economies, increasing adoption of AI technologies, and supportive government initiatives in countries like China and India are driving the market expansion in this region.

Component Analysis

The AI Data Resource Service market can be segmented by component into software, hardware, and services. Each of these components plays a critical role in the overall functionality and effectiveness of AI data resource solutions, and their demand varies across different industries and applications.

In the software segment, the market is driven by the increasing adoption of AI-driven analytics solutions and data management platforms. These solutions enable organizations to efficiently process and analyze large volumes of data, derive actionable insights, and enhance their decision-making processes. The continuous advancements in AI algorithms and the development of new software tools are further propelling the growth of this segment.

The hardware segment is also witnessing significant growth due to the rising demand for high-performance computing systems, storage solutions, and data centers. These hardware components are essential for supporting the extensive computational requirements of AI data processing tasks. With the proliferation of big data and the increasing complexity of AI models, the need for advanced hardware infrastructure is becoming more critical, driving the growth of this segment.

The services segment encompasses various professional and managed services that assist organizations in implementing, maintaining, and optimizing their AI data resource solutions. This includes consulting services, system integration, training, and support services. The growing complexity of AI technologies and the need for specialized expertise are driving the demand for these services. Organizations are increasingly relying on external service providers to ensure the successful deployment and operation of their AI data resources.

Overall,

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

AI Training Dataset Market Outlook

The global AI training dataset market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This substantial growth is driven by the increasing adoption of artificial intelligence across various industries, the necessity for large-scale and high-quality datasets to train AI models, and the ongoing advancements in AI and machine learning technologies.

One of the primary growth factors in the AI training dataset market is the exponential increase in data generation across multiple sectors. With the proliferation of internet usage, the expansion of IoT devices, and the digitalization of industries, there is an unprecedented volume of data being generated daily. This data is invaluable for training AI models, enabling them to learn and make more accurate predictions and decisions. Moreover, the need for diverse and comprehensive datasets to improve AI accuracy and reliability is further propelling market growth.

Another significant factor driving the market is the rising investment in AI and machine learning by both public and private sectors. Governments around the world are recognizing the potential of AI to transform economies and improve public services, leading to increased funding for AI research and development. Simultaneously, private enterprises are investing heavily in AI technologies to gain a competitive edge, enhance operational efficiency, and innovate new products and services. These investments necessitate high-quality training datasets, thereby boosting the market.

The proliferation of AI applications in various industries, such as healthcare, automotive, retail, and finance, is also a major contributor to the growth of the AI training dataset market. In healthcare, AI is being used for predictive analytics, personalized medicine, and diagnostic automation, all of which require extensive datasets for training. The automotive industry leverages AI for autonomous driving and vehicle safety systems, while the retail sector uses AI for personalized shopping experiences and inventory management. In finance, AI assists in fraud detection and risk management. The diverse applications across these sectors underline the critical need for robust AI training datasets.

As the demand for AI applications continues to grow, the role of Ai Data Resource Service becomes increasingly vital. These services provide the necessary infrastructure and tools to manage, curate, and distribute datasets efficiently. By leveraging Ai Data Resource Service, organizations can ensure that their AI models are trained on high-quality and relevant data, which is crucial for achieving accurate and reliable outcomes. The service acts as a bridge between raw data and AI applications, streamlining the process of data acquisition, annotation, and validation. This not only enhances the performance of AI systems but also accelerates the development cycle, enabling faster deployment of AI-driven solutions across various sectors.

Regionally, North America currently dominates the AI training dataset market due to the presence of major technology companies and extensive R&D activities in the region. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid technological advancements, increasing investments in AI, and the growing adoption of AI technologies across various industries in countries like China, India, and Japan. Europe and Latin America are also anticipated to experience significant growth, supported by favorable government policies and the increasing use of AI in various sectors.

Data Type Analysis

The data type segment of the AI training dataset market encompasses text, image, audio, video, and others. Each data type plays a crucial role in training different types of AI models, and the demand for specific data types varies based on the application. Text data is extensively used in natural language processing (NLP) applications such as chatbots, sentiment analysis, and language translation. As the use of NLP is becoming more widespread, the demand for high-quality text datasets is continually rising. Companies are investing in curated text datasets that encompass diverse languages and dialects to improve the accuracy and efficiency of NLP models.

Image data is critical for computer vision application

As of November 2019, application-specific integrated circuits (ASIC) are forecast to have a growing share of the training phase artificial intelligence (AI) applications in data centers, making up for a projected ** percent of it by 2025. Comparatively, graphics processing units (GPUs) will lose their presence by that time, dropping from ** percent down to ** percent. AI chips In order to provide greater security and efficiency, many data centers are overseeing the widespread implementation of artificial intelligence (AI) in their processes and systems. AI technologies and tasks require specialized AI chips that are more powerful and optimized for advanced machine learning (ML) algorithms, owning to an overall growth in data center chip revenues. The edge An interesting development for the data center industry is the rise of the edge computing. IT infrastructure is moved into edge data centers, specialized facilities that are located nearer to end-users. The global edge data center market size is expected to reach **** billion U.S. dollars in 2024, twice the size of the market in 2020, with experts suggesting that the growth of emerging technologies like 5G and IoT will contribute to this growth.

This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases.

Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public.

In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories.

https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government

The Generative AI Market size was valued at USD 16.88 billion in 2023 and is projected to reach USD 149.04 billion by 2032, exhibiting a CAGR of 36.5 % during the forecasts period. The generative AI market specifically means the segment of a market that sells products based on the AI technologies for creating content that includes text, images, audio content, and videos. While generative AI models are mainly based on machine learning, especially neural networks, it synthesises new content that is similar to human-generated data. Some of them are as follows- Creation of contents and designs, more specifically in discovery of any drug and through customized marketing strategies. It is applied to areas including, but not limited to entertainment, health care, and finances. Modern developments indicate the emergence of AI-art, AI-music, and AI-writings, the usage of generative AI for automated communication with customers, and the enhancement of AI-ethics and -regulations. Challenges are defined by the constant enhancements in AI algorithms and the rising need for automation and inventiveness in various fields. Recent developments include: In April 2023, Microsoft Corp. collaborated with Epic Systems, an American healthcare software company, to incorporate large language model tools and AI into Epic’s electronic health record software. This partnership aims to use generative AI to help healthcare providers increase productivity while reducing administrative burden , In March 2021, MOSTLY AI Inc. announced its partnership with Erste Group, an Australian bank to provide its AI-based synthetic data solution. Using synthetic data, Erste Group aims to boost its digital banking innovation and enable data-based development .

We offer comprehensive data collection services that cater to a wide range of industries and applications. Whether you require image, audio, or text data, we have the expertise and resources to collect and deliver high-quality data that meets your specific requirements. Our data collection methods include manual collection, web scraping, and other automated techniques that ensure accuracy and completeness of data.

Our team of experienced data collectors and quality assurance professionals ensure that the data is collected and processed according to the highest standards of quality. We also take great care to ensure that the data we collect is relevant and applicable to your use case. This means that you can rely on us to provide you with clean and useful data that can be used to train machine learning models, improve business processes, or conduct research.

We are committed to delivering data in the format that you require. Whether you need raw data or a processed dataset, we can deliver the data in your preferred format, including CSV, JSON, or XML. We understand that every project is unique, and we work closely with our clients to ensure that we deliver the data that meets their specific needs. So if you need reliable data collection services for your next project, look no further than us.

Artificial Intelligence Market Outlook

According to our latest research, the global Artificial Intelligence (AI) market size reached USD 215.8 billion in 2024, demonstrating robust expansion driven by rapid digital transformation across key sectors. The market is projected to grow at a CAGR of 36.6% between 2025 and 2033, reaching a forecasted value of USD 2,870.1 billion by 2033. This remarkable growth trajectory is fueled by increasing adoption of AI-powered solutions in industries such as healthcare, finance, manufacturing, and retail, as well as advancements in machine learning, deep learning, and natural language processing technologies.

The primary growth factor for the Artificial Intelligence market is the accelerating integration of AI technologies into business operations to enhance productivity, automate repetitive tasks, and enable data-driven decision-making. Organizations are increasingly leveraging AI-based tools to streamline workflows, reduce operational costs, and improve customer experiences. The proliferation of big data and the need for advanced analytics have further amplified the demand for AI solutions, as businesses seek to extract actionable insights from massive volumes of structured and unstructured data. Additionally, the growing availability of affordable computing power and cloud-based AI platforms has democratized access to advanced AI capabilities, enabling companies of all sizes to deploy intelligent solutions at scale.

Another significant driver propelling the AI market is the rapid evolution of AI technologies themselves. Innovations in areas such as machine learning, computer vision, and natural language processing are paving the way for more sophisticated and versatile AI applications across industries. For instance, AI-powered diagnostic tools are revolutionizing healthcare by enabling earlier and more accurate disease detection, while intelligent automation is transforming manufacturing processes through predictive maintenance and quality assurance. The rise of AI-powered virtual assistants and chatbots has also enhanced customer engagement in sectors like retail and banking, providing personalized and efficient service around the clock. The convergence of AI with other emerging technologies, such as the Internet of Things (IoT) and edge computing, is further expanding the potential use cases for AI, driving deeper market penetration.

Strategic investments and supportive government initiatives are playing a pivotal role in fostering the growth of the AI market. Governments across the globe are recognizing the transformative potential of AI and are investing heavily in research and development, talent development, and digital infrastructure. Public-private partnerships, favorable regulatory frameworks, and targeted funding programs are accelerating AI innovation and adoption, particularly in regions like North America, Europe, and Asia Pacific. Moreover, the emergence of AI startups and the increasing collaborations between technology giants and industry players are catalyzing the creation of new AI-driven products and services, further stimulating market expansion.

From a regional perspective, North America continues to dominate the global Artificial Intelligence market, accounting for the largest share in 2024. The region's leadership is attributed to its advanced digital ecosystem, concentration of leading AI technology providers, and strong investment climate. However, Asia Pacific is emerging as a high-growth market, driven by rapid digitalization, expanding internet penetration, and significant investments in AI research and development by countries such as China, Japan, and South Korea. Europe is also witnessing substantial growth, supported by robust regulatory frameworks, government initiatives, and a thriving innovation ecosystem. Meanwhile, Latin America and the Middle East & Africa are gradually embracing AI technologies, with increasing adoption in sectors such as banking, healthcare, and government services.

Component Analysis

Introducing a comprehensive and openly accessible dataset designed for researchers and data scientists in the field of artificial intelligence. This dataset encompasses a collection of over 4,000 AI tools, meticulously categorized into more than 50 distinct categories. This valuable resource has been generously shared by its owner, TasticAI, and is freely available for various purposes such as research, benchmarking, market surveys, and more. Dataset Overview: The dataset provides an extensive repository of AI tools, each accompanied by a wealth of information to facilitate your research endeavors. Here is a brief overview of the key components: AI Tool Name: Each AI tool is listed with its name, providing an easy reference point for users to identify specific tools within the dataset. Description: A concise one-line description is provided for each AI tool. This description offers a quick glimpse into the tool's purpose and functionality. AI Tool Category: The dataset is thoughtfully organized into more than 50 distinct categories, ensuring that you can easily locate AI tools that align with your research interests or project needs. Whether you are working on natural language processing, computer vision, machine learning, or other AI subfields, you will find a dedicated category. Images: Visual representation is crucial for understanding and identifying AI tools. To aid your exploration, the dataset includes images associated with each tool, allowing for quick recognition and visual association. Website Links: Accessing more detailed information about a specific AI tool is effortless, as direct links to the tool's respective website or documentation are provided. This feature enables researchers and data scientists to delve deeper into the tools that pique their interest. Utilization and Benefits: This openly shared dataset serves as a valuable resource for various purposes: Research: Researchers can use this dataset to identify AI tools relevant to their studies, facilitating faster literature reviews, comparative analyses, and the exploration of cutting-edge technologies. Benchmarking: The extensive collection of AI tools allows for comprehensive benchmarking, enabling you to evaluate and compare tools within specific categories or across categories. Market Surveys: Data scientists and market analysts can utilize this dataset to gain insights into the AI tool landscape, helping them identify emerging trends and opportunities within the AI market. Educational Purposes: Educators and students can leverage this dataset for teaching and learning about AI tools, their applications, and the categorization of AI technologies. Conclusion: In summary, this openly shared dataset from TasticAI, featuring over 4,000 AI tools categorized into more than 50 categories, represents a valuable asset for researchers, data scientists, and anyone interested in the field of artificial intelligence. Its easy accessibility, detailed information, and versatile applications make it an indispensable resource for advancing AI research, benchmarking, market analysis, and more. Explore the dataset at https://tasticai.com and unlock the potential of this rich collection of AI tools for your projects and studies.

Xverum’s AI & ML Training Data provides one of the most extensive datasets available for AI and machine learning applications, featuring 800M B2B profiles with 100+ attributes. This dataset is designed to enable AI developers, data scientists, and businesses to train robust and accurate ML models. From natural language processing (NLP) to predictive analytics, our data empowers a wide range of industries and use cases with unparalleled scale, depth, and quality.

What Makes Our Data Unique?

Scale and Coverage: - A global dataset encompassing 800M B2B profiles from a wide array of industries and geographies. - Includes coverage across the Americas, Europe, Asia, and other key markets, ensuring worldwide representation.

Rich Attributes for Training Models: - Over 100 fields of detailed information, including company details, job roles, geographic data, industry categories, past experiences, and behavioral insights. - Tailored for training models in NLP, recommendation systems, and predictive algorithms.

Compliance and Quality: - Fully GDPR and CCPA compliant, providing secure and ethically sourced data. - Extensive data cleaning and validation processes ensure reliability and accuracy.

Annotation-Ready: - Pre-structured and formatted datasets that are easily ingestible into AI workflows. - Ideal for supervised learning with tagging options such as entities, sentiment, or categories.

How Is the Data Sourced? - Publicly available information gathered through advanced, GDPR-compliant web aggregation techniques. - Proprietary enrichment pipelines that validate, clean, and structure raw data into high-quality datasets. This approach ensures we deliver comprehensive, up-to-date, and actionable data for machine learning training.

Primary Use Cases and Verticals

Natural Language Processing (NLP): Train models for named entity recognition (NER), text classification, sentiment analysis, and conversational AI. Ideal for chatbots, language models, and content categorization.

Predictive Analytics and Recommendation Systems: Enable personalized marketing campaigns by predicting buyer behavior. Build smarter recommendation engines for ecommerce and content platforms.

B2B Lead Generation and Market Insights: Create models that identify high-value leads using enriched company and contact information. Develop AI systems that track trends and provide strategic insights for businesses.

HR and Talent Acquisition AI: Optimize talent-matching algorithms using structured job descriptions and candidate profiles. Build AI-powered platforms for recruitment analytics.

How This Product Fits Into Xverum’s Broader Data Offering Xverum is a leading provider of structured, high-quality web datasets. While we specialize in B2B profiles and company data, we also offer complementary datasets tailored for specific verticals, including ecommerce product data, job listings, and customer reviews. The AI Training Data is a natural extension of our core capabilities, bridging the gap between structured data and machine learning workflows. By providing annotation-ready datasets, real-time API access, and customization options, we ensure our clients can seamlessly integrate our data into their AI development processes.

Why Choose Xverum? - Experience and Expertise: A trusted name in structured web data with a proven track record. - Flexibility: Datasets can be tailored for any AI/ML application. - Scalability: With 800M profiles and more being added, you’ll always have access to fresh, up-to-date data. - Compliance: We prioritize data ethics and security, ensuring all data adheres to GDPR and other legal frameworks.

Ready to supercharge your AI and ML projects? Explore Xverum’s AI Training Data to unlock the potential of 800M global B2B profiles. Whether you’re building a chatbot, predictive algorithm, or next-gen AI application, our data is here to help.

Contact us for sample datasets or to discuss your specific needs.

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Artificial Intelligence Platforms Market Size 2024-2028

The artificial intelligence platforms market size is forecast to increase by USD 64.9 billion at a CAGR of 45.1% between 2023 and 2028. The market is experiencing significant growth due to the rising demand for AI-based solutions in various industries. Businesses are increasingly adopting AI technologies to automate processes, enhance productivity, and improve customer experiences. Another trend driving AI platforms market growth is the increasing interoperability among neural networks, enabling seamless data exchange and collaboration between different AI systems. However, the market also faces challenges such as the rise in data privacy issues and ethical concerns related to AI usage. As data becomes a valuable asset, ensuring its security and privacy is paramount for businesses implementing AI solutions. This dynamic market landscape underscores the critical role of artificial intelligence platforms in driving innovation and efficiency across various sectors such as education and telecommunications. Additionally, there is a need for clear regulations and guidelines to address ethical concerns and ensure transparency in AI decision-making. Overall, the market for artificial intelligence platforms is expected to continue its growth trajectory, driven by these trends and challenges.

What will be the Size of the Artificial Intelligence Platforms Market During the Forecast Period?

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Artificial Intelligence Platforms Market Segmentation

The AI platforms market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Deployment Outlook 

  On-premise
  Cloud-based


Application Outlook

  Retail
  Banking
  Manufacturing
  Healthcare
  Others


Region Outlook

  North America

    U.S.
    Canada





  Europe

    U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India





  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa


  South America

    Chile
    Brazil
    Argentina

By Application Insights

The retail segment is estimated to witness significant growth during the forecast period. Artificial intelligence (AI) is revolutionizing various industries by enabling advanced data processing, pattern identification, and decision-making capabilities. In healthcare, AI is used for medical imaging analysis, drug discovery, and patient care. In the food and beverages sector, AI is employed for supply chain optimization and product innovation. Digital technologies, including AI software, are transforming banking by facilitating algorithmic trading, fraud detection, and credit risk assessment.

Industry adoption of AI is also prominent in business intelligence, customer experience, and operational efficiency. The emergence of technologies such as big data, IoT, customer relationship management (CRM), and workflow automation are accelerating technological transformations in the sector. AI is used to provide personalized recommendations, automate processes, and optimize workflows. Intelligent virtual assistants, chatbots, natural language processing, speech recognition, and conversational AI interactions are increasingly being used to enhance customer experience.

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The retail segment accounted for USD 662.60 million in 2018. Industry-specific AI Solutions are being developed for finance, where they are used for regulatory support, ethical considerations, data privacy, and security concerns. AI as a service (AIaaS) and cloud computing platforms are enabling businesses to leverage AI capabilities without having to build and maintain their own infrastructure.

Autonomous systems are being adopted for process optimization in manufacturing and logistics. In conclusion, AI is transforming industries by enabling advanced data processing, pattern identification, and decision-making capabilities. Its applications include healthcare, food and beverages, banking, business intelligence, customer experience, and operational efficiency. AI is also being used to develop industry-specific solutions for finance, and to enable autonomous systems for process optimization. Despite the numerous benefits, ethical considerations, data privacy, and security concerns remain key challenges.

Regional Analysis

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North America is estimated to contribute 66% to the growth of the global artificial intelligence platforms market during the market forecast period. Technavio's analysts have elaborately explained the regional trends an

Our data was collected for a project on how media representations shape public perceptions of AI and then use what we learn to explore how we might better represent everyday interactions with AI to the public. We began by developing lists, taxonomies, and case studies of popular representations of AI and hypotheses about how the general public and elite groups discriminate between “good” and “bad” AI, and about how media representations help shape these perceptions. Our research questions include:What drives popular negativity about AI? Does the public have its reasons that the experts know not of? Or have the public adopted views of AI borne of misrepresentations?Do overtly dystopian representations of AI feed, or perhaps temper, public outrage about insidious issues with AI and machine learning such as the biases of search algorithms?Can we produce narratives on AI in different media modalities that are more nuanced and complex than just the false dichotomy of good and bad? Could a more accurately critical (and yet still exciting) model of AI themed entertainment be developed, once we’ve gained an understanding of how the public has been encountering AI?

A 2024 survey carried out in the United States showed that nearly one in two consumers would not allow artificial intelligence (AI) to access their personal data for personalization. While 16 percent of the surveyed consumers were not too sure about it, about the same percentage of shoppers would allow AI technologies to access their information details to get a more convenient and personalized shopping experience.

Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata: This data product is a unique offering in the realm of AI/ML training data. What sets it apart is the sheer volume and diversity of the dataset, which includes 4.5 million files spanning across 20 different categories. These categories range from Animals/Wildlife and The Arts to Technology and Transportation, providing a rich and varied dataset for AI/ML applications.

The data is sourced from Wirestock's platform, where creators upload and sell their photos, videos, and AI art online. This means that the data is not only vast but also constantly updated, ensuring a fresh and relevant dataset for your AI/ML needs. The data is collected in a GDPR-compliant manner, ensuring the privacy and rights of the creators are respected.

The primary use-cases for this data product are numerous. It is ideal for training machine learning models for image recognition, improving computer vision algorithms, and enhancing AI applications in various industries such as retail, healthcare, and transportation. The diversity of the dataset also means it can be used for more niche applications, such as training AI to recognize specific objects or scenes.

This data product fits into Wirestock's broader data offering as a key resource for AI/ML training. Wirestock is a platform for creators to sell their work, and this dataset is a collection of that work. It represents the breadth and depth of content available on Wirestock, making it a valuable resource for any company working with AI/ML.

The core benefits of this dataset are its volume, diversity, and quality. With 4.5 million files, it provides a vast resource for AI training. The diversity of the dataset, spanning 20 categories, ensures a wide range of images for training purposes. The quality of the images is also high, as they are sourced from creators selling their work on Wirestock.

In terms of how the data is collected, creators upload their work to Wirestock, where it is then sold on various marketplaces. This means the data is sourced directly from creators, ensuring a diverse and unique dataset. The data includes both the images themselves and associated metadata, providing additional context for each image.

The different image categories included in this dataset are Animals/Wildlife, The Arts, Backgrounds/Textures, Beauty/Fashion, Buildings/Landmarks, Business/Finance, Celebrities, Education, Emotions, Food Drinks, Holidays, Industrial, Interiors, Nature Parks/Outdoor, People, Religion, Science, Signs/Symbols, Sports/Recreation, Technology, Transportation, Vintage, Healthcare/Medical, Objects, and Miscellaneous. This wide range of categories ensures a diverse dataset that can cater to a variety of AI/ML applications.

Artificial Intelligence Model Market Outlook

The global artificial intelligence (AI) model market size was valued at approximately $47.5 billion in 2023 and is projected to reach around $390 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 26.7% during the forecast period. This significant growth is driven by advancements in AI technologies and the increasing adoption of AI across various sectors, including healthcare, finance, and retail.

One of the primary growth factors for the AI model market is the rising demand for automation and efficiency across industries. Organizations are increasingly relying on AI models to streamline operations, enhance productivity, and reduce operational costs. The integration of AI models with existing business processes enables companies to make data-driven decisions, optimize supply chains, and improve customer experiences. The rapid evolution of machine learning algorithms and the availability of vast amounts of data are further fueling the adoption of AI models.

Another critical driver is the significant investments in AI research and development by both public and private sectors. Governments worldwide are recognizing the potential of AI to drive economic growth and are funding various AI initiatives. Simultaneously, tech giants like Google, Microsoft, and IBM are investing heavily in AI research to develop cutting-edge AI models and solutions. These investments are accelerating innovation in AI technologies and expanding the market's growth prospects.

The proliferation of cloud computing is also a substantial growth factor for the AI model market. Cloud-based AI solutions offer scalability, flexibility, and cost-effectiveness, making them attractive to businesses of all sizes. The cloud enables organizations to access sophisticated AI tools and models without the need for significant upfront investments in hardware and software. As a result, the adoption of cloud-based AI models is rapidly increasing, particularly among small and medium enterprises (SMEs).

Regionally, North America holds the largest share of the AI model market, driven by the presence of major technology companies and robust research infrastructure. The region's strong focus on innovation and early adoption of AI technologies contribute to its market dominance. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as rapid industrialization, increasing investments in AI, and the growing adoption of AI solutions by businesses in countries like China, India, and Japan are driving this growth.

Component Analysis

The AI model market can be segmented by component into software, hardware, and services. The software segment is the largest and fastest-growing component, driven by the increasing demand for AI platforms and applications. AI software includes machine learning frameworks, natural language processing tools, and computer vision applications, all of which are essential for developing and deploying AI models. The continuous advancements in these software tools are enabling more sophisticated AI models and expanding their applicability across different sectors.

The hardware segment includes AI-specific processors, GPUs, and specialized hardware designed to accelerate AI computations. As AI models become more complex and data-intensive, the demand for high-performance hardware is rising. Companies are investing in advanced hardware to support AI workloads and improve the efficiency of AI model training and inference. Innovations in AI hardware, such as neuromorphic computing and quantum processors, are expected to further enhance the performance of AI models.

The services segment comprises consulting, implementation, and maintenance services related to AI models. As organizations adopt AI technologies, they require expertise to integrate AI models into their existing systems and processes. Consulting services help businesses identify suitable AI solutions and develop strategies for AI adoption. Implementation services assist in deploying and configuring AI models, while maintenance services ensure the ongoing performance and reliability of AI systems. The growing complexity of AI technologies and the need for specialized knowledge are driving the demand for AI-related services.

Report Scope

IntroductionProviding one-on-one support to large cohorts is challenging, yet emerging AI technologies show promise in bridging the gap between the support students want and what educators can provide. They offer students a way to engage with their course material in a way that feels fluent and instinctive. Whilst educators may have views on the appropriates for AI, the tools themselves, as well as the novel ways in which they can be used, are continually changing.MethodsThe aim of this study was to probe students' familiarity with AI tools, their views on its current uses, their understanding of universities' AI policies, and finally their impressions of its importance, both to their degree and their future careers. We surveyed 453 psychology and sport science students across two institutions in the UK, predominantly those in the first and second year of undergraduate study, and conducted a series of five focus groups to explore the emerging themes of the survey in more detail.ResultsOur results showed a wide range of responses in terms of students' familiarity with the tools and what they believe AI tools could and should not be used for. Most students emphasized the importance of understanding how AI tools function and their potential applications in both their academic studies and future careers. The results indicated a strong desire among students to learn more about AI technologies. Furthermore, there was a significant interest in receiving dedicated support for integrating these tools into their coursework, driven by the belief that such skills will be sought after by future employers. However, most students were not familiar with their university's published AI policies.DiscussionThis research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology and suggests a need for a more comprehensive approach to communicating these important guidelines on an on-going basis, especially as the tools and guidelines evolve.