100+ datasets found
  1. d

    Factori Machine Learning (ML) Data | 247 Countries Coverage | 5.2 B Event...

    • datarade.ai
    .csv
    Updated Oct 1, 2019
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    Factori (2019). Factori Machine Learning (ML) Data | 247 Countries Coverage | 5.2 B Event per Day [Dataset]. https://datarade.ai/data-products/factori-ai-ml-training-data-web-data-machine-learning-d-factori
    Explore at:
    .csvAvailable download formats
    Dataset updated
    Oct 1, 2019
    Dataset authored and provided by
    Factori
    Area covered
    Cameroon, Sweden, Egypt, Faroe Islands, Turks and Caicos Islands, Japan, Austria, Uzbekistan, Taiwan, Palestine
    Description

    Factori's AI & ML training data is thoroughly tested and reviewed to ensure that what you receive on your end is of the best quality.

    Integrate the comprehensive AI & ML training data provided by Grepsr and develop a superior AI & ML model.

    Whether you're training algorithms for natural language processing, sentiment analysis, or any other AI application, we can deliver comprehensive datasets tailored to fuel your machine learning initiatives.

    Enhanced Data Quality: We have rigorous data validation processes and also conduct quality assurance checks to guarantee the integrity and reliability of the training data for you to develop the AI & ML models.

    Gain a competitive edge, drive innovation, and unlock new opportunities by leveraging the power of tailored Artificial Intelligence and Machine Learning training data with Factori.

    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.

    Web Data Reach: Our reach data represents the total number of data counts available within various categories and comprises attributes such as Country, Anonymous ID, IP addresses, Search Query, and so on.

    Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

    Data Attributes: Anonymous_id IDType Timestamp Estid Ip userAgent browserFamily deviceType Os Url_metadata_canonical_url Url_metadata_raw_query_params refDomain mappedEvent Channel searchQuery Ttd_id Adnxs_id Keywords Categories Entities Concepts

  2. Artificial Intelligence (AI) Training Dataset Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Jun 30, 2025
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    Growth Market Reports (2025). Artificial Intelligence (AI) Training Dataset Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/artificial-intelligence-training-dataset-market-global-industry-analysis
    Explore at:
    pptx, csv, pdfAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    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

  3. G

    Generative AI Market Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Jan 2, 2025
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    Market Research Forecast (2025). Generative AI Market Report [Dataset]. https://www.marketresearchforecast.com/reports/generative-ai-market-1667
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    Jan 2, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    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 .

  4. D

    Notable AI Models

    • epoch.ai
    csv
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    Epoch AI, Notable AI Models [Dataset]. https://epoch.ai/data/notable-ai-models
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    csvAvailable download formats
    Dataset authored and provided by
    Epoch AI
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Global
    Variables measured
    https://epoch.ai/data/notable-ai-models-documentation#records
    Measurement technique
    https://epoch.ai/data/notable-ai-models-documentation#records
    Description

    Our most comprehensive database of AI models, containing over 800 models that are state of the art, highly cited, or otherwise historically notable. It tracks key factors driving machine learning progress and includes over 300 training compute estimates.

  5. c

    AI Training Data Market will grow at a CAGR of 23.50% from 2024 to 2031.

    • cognitivemarketresearch.com
    pdf,excel,csv,ppt
    Updated May 15, 2025
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    Cognitive Market Research (2025). AI Training Data Market will grow at a CAGR of 23.50% from 2024 to 2031. [Dataset]. https://www.cognitivemarketresearch.com/ai-training-data-market-report
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    May 15, 2025
    Dataset authored and provided by
    Cognitive Market Research
    License

    https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy

    Time period covered
    2021 - 2033
    Area covered
    Global
    Description

    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.

    (Source: about:blank)

    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...

  6. Data center chip architecture used for AI training phase 2017-2025

    • statista.com
    Updated Jul 1, 2025
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    Statista (2025). Data center chip architecture used for AI training phase 2017-2025 [Dataset]. https://www.statista.com/statistics/1104879/data-center-chip-architecture-for-ai-training/
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    Dataset updated
    Jul 1, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2017
    Area covered
    Worldwide
    Description

    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.

  7. Artificial Intelligence (AI) in Medical Market Report | Global Forecast From...

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 12, 2024
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    Dataintelo (2024). Artificial Intelligence (AI) in Medical Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-artificial-intelligence-ai-in-medical-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Sep 12, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Artificial Intelligence (AI) in Medical Market Outlook



    The global market size for Artificial Intelligence (AI) in the medical sector was valued at approximately $5.2 billion in 2023 and is expected to reach around $45.2 billion by 2032, growing at a CAGR of 26.8% during the forecast period. This remarkable growth can be attributed to several factors, including advancements in AI technology, increasing healthcare data, and the rising demand for personalized medicine.



    One of the primary growth factors for AI in the medical market is the rapid advancement in AI technologies such as machine learning, natural language processing, and computer vision. These technologies enable healthcare providers to analyze vast amounts of data more effectively, leading to improved diagnostic accuracy and better patient outcomes. The integration of AI in medical imaging, for example, aids radiologists in detecting anomalies much earlier, thus facilitating timely intervention and treatment.



    Another significant driver is the growing volume of healthcare data generated from electronic health records (EHRs), wearable devices, and genomics. AI systems are highly efficient at processing and analyzing this data to extract meaningful insights, which can be used for predictive analytics and early disease detection. This capability not only enhances patient care but also contributes to the operational efficiency of healthcare providers by streamlining administrative tasks and reducing the risk of human error.



    The rising demand for personalized medicine is also a crucial factor driving the market. AI algorithms can analyze individual patient data to provide customized treatment plans, improving the efficacy of medical interventions. This approach is particularly beneficial in oncology, where personalized treatment plans based on genetic profiling can significantly improve patient outcomes. Furthermore, AI's role in drug discovery and development is accelerating the process of bringing new drugs to market, thus addressing unmet medical needs more rapidly.



    Regionally, North America holds the largest share of the AI in medical market, primarily due to the high adoption rate of advanced technologies and substantial investments in healthcare infrastructure. The presence of key market players and extensive research activities further bolster the market in this region. Europe follows closely, with significant contributions from countries like Germany, the UK, and France, which have well-established healthcare systems and a strong focus on innovation. The Asia Pacific region is anticipated to witness the highest growth rate, driven by increasing healthcare expenditure, growing awareness of AI applications in healthcare, and supportive government policies.



    Component Analysis



    The AI in medical market can be segmented by component into software, hardware, and services. The software segment holds the largest market share and is expected to continue its dominance during the forecast period. This segment includes AI algorithms, platforms, and analytical tools that are crucial for data analysis and decision-making processes in medical applications. The growing adoption of AI-based software solutions for diagnostic and predictive analytics is a key driver for this segment.



    The hardware segment, although smaller than software, is also experiencing significant growth. This segment comprises AI-enabled medical devices, sensors, and computing infrastructure necessary to support AI applications. The increasing demand for advanced imaging systems, robotic surgical instruments, and AI-integrated diagnostic tools is propelling the growth of this segment. Furthermore, advancements in hardware technologies, such as the development of high-performance GPUs and specialized AI chips, are enhancing the capabilities of AI systems in healthcare.



    The services segment encompasses various support services required for the implementation and maintenance of AI systems in medical settings. This includes consulting, integration, and training services provided by vendors to ensure the smooth deployment and operation of AI technologies. The growing need for specialized skills and expertise to manage AI systems, along with the rising trend of outsourcing these services, is driving the expansion of this segment. Additionally, ongoing support and maintenance services are essential to keep AI systems updated and functioning optimally.



    Within the services segment, managed services are gaining traction as healthcare providers seek to minimize the complexi

  8. d

    Machine Learning (ML) Data | 800M+ B2B Profiles | AI-Ready for Deep Learning...

    • datarade.ai
    .json, .csv
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    Xverum, Machine Learning (ML) Data | 800M+ B2B Profiles | AI-Ready for Deep Learning (DL), NLP & LLM Training [Dataset]. https://datarade.ai/data-products/xverum-company-data-b2b-data-belgium-netherlands-denm-xverum
    Explore at:
    .json, .csvAvailable download formats
    Dataset provided by
    Xverum LLC
    Authors
    Xverum
    Area covered
    Norway, Barbados, Dominican Republic, United Kingdom, Western Sahara, Sint Maarten (Dutch part), Cook Islands, Oman, Jordan, India
    Description

    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.

  9. Artificial Intelligence Platforms Market Analysis North America, APAC,...

    • technavio.com
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    Technavio, Artificial Intelligence Platforms Market Analysis North America, APAC, Europe, South America, Middle East and Africa - US, China, Germany, UK, France - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/artificial-intelligence-platforms-market-size-industry-analysis
    Explore at:
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, United States
    Description

    Snapshot img

    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?

    To learn more about the AI platforms market report, Request Free Sample

    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.

    Get a glance at the market share of various regions. Download the PDF Sample

    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

    For more insights on the market share of various regions, Download PDF Sample now!

    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

  10. AI access to personal data when shopping in the U.S. 2024

    • statista.com
    • davegsmith.com
    Updated Jun 4, 2024
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    Statista (2024). AI access to personal data when shopping in the U.S. 2024 [Dataset]. https://www.statista.com/statistics/1470189/ai-access-to-personal-data-for-personalized-shopping-experience-united-states/
    Explore at:
    Dataset updated
    Jun 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Feb 2024
    Area covered
    United States
    Description

    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.

  11. Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata

    • datarade.ai
    .csv
    Updated Jul 18, 2023
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    WIRESTOCK (2023). Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata [Dataset]. https://datarade.ai/data-products/wirestock-s-ai-ml-image-training-data-4-5m-files-with-metadata-wirestock
    Explore at:
    .csvAvailable download formats
    Dataset updated
    Jul 18, 2023
    Dataset provided by
    Wirestock
    Authors
    WIRESTOCK
    Area covered
    Belarus, Chile, Sudan, Estonia, Peru, Jersey, Pakistan, New Caledonia, Swaziland, Georgia
    Description

    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.

  12. AI Training Dataset Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Jan 7, 2025
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    Dataintelo (2025). AI Training Dataset Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-ai-training-dataset-market
    Explore at:
    csv, pptx, pdfAvailable download formats
    Dataset updated
    Jan 7, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    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

  13. f

    Data Sheet 1_The impact of AI on education and careers: What do students...

    • frontiersin.figshare.com
    docx
    Updated Nov 14, 2024
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    Sarah R. Thomson; Beverley Ann Pickard-Jones; Stephanie Baines; Pauldy C. J. Otermans (2024). Data Sheet 1_The impact of AI on education and careers: What do students think?.docx [Dataset]. http://doi.org/10.3389/frai.2024.1457299.s001
    Explore at:
    docxAvailable download formats
    Dataset updated
    Nov 14, 2024
    Dataset provided by
    Frontiers
    Authors
    Sarah R. Thomson; Beverley Ann Pickard-Jones; Stephanie Baines; Pauldy C. J. Otermans
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  14. Artificial Intelligence (AI) Infrastructure Market Analysis North America,...

    • technavio.com
    Updated Oct 15, 2024
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    Technavio (2024). Artificial Intelligence (AI) Infrastructure Market Analysis North America, Europe, APAC, South America, Middle East and Africa - US, China, UK, Japan, Germany - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/ai-infrastructure-market-industry-analysis
    Explore at:
    Dataset updated
    Oct 15, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, United States
    Description

    Snapshot img

    Artificial Intelligence (AI) Infrastructure Market Size 2024-2028

    The artificial intelligence (ai) infrastructure market size is forecast to increase by USD 22.07 billion at a CAGR of 20.6% between 2023 and 2028.

    The market is experiencing significant growth, driven by the emerging application of machine learning (ML) in various industries. The increasing availability of cloud-based AI applications is also fueling market expansion. However, privacy concerns associated with AI deployment pose a challenge to market growth. As ML algorithms collect and process vast amounts of data, ensuring data security and privacy becomes crucial. Despite these challenges, the market is expected to continue its growth trajectory, driven by advancements in AI technologies and their increasing adoption across sectors. The implementation of robust data security measures and regulatory frameworks will be essential to address privacy concerns and foster market growth.

    What will be the Size of the Artificial Intelligence (AI) Infrastructure Market During the Forecast Period?

    Request Free SampleThe market encompasses the hardware and software solutions required to build, train, deploy, and scale AI models. Key market drivers include the increasing demand for machine learning workloads, data processing for various applications such as image recognition and natural language processing, and the need for computational power and networking capabilities to handle large data sets. The market is characterized by continuous improvement and competitive advantage through the use of GPUs and TPUs for AI algorithms, as well as cloud computing solutions offering high-bandwidth and scalability. Security is a critical consideration, with data handling and storage solutions implementing robust encryption and access control measures.AI infrastructure is utilized across diverse industries, including healthcare and finance, to drive innovation and precision medicine, and to enhance operational efficiency and productivity. Data processing frameworks play a pivotal role in facilitating the deployment and scaling of AI models, enabling organizations to maintain flexibility and adapt to evolving business needs.

    How is this Artificial Intelligence (AI) Infrastructure Industry segmented and which is the largest segment?

    The artificial intelligence (ai) infrastructure industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. TypeProcessorStorageMemoryGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanSouth AmericaMiddle East and Africa

    By Type Insights

    The processor segment is estimated to witness significant growth during the forecast period.
    

    The market is experiencing significant growth due to the increasing adoption of AI and machine learning (ML) technologies across various industries. The market encompasses hardware, software, machine learning workloads, data processing, model training, deployment, scalability, flexibility, security, and computational power. Hardware solutions include GPUs and TPUs, while software solutions consist of data processing frameworks, image recognition, natural language processing, and AI algorithms. Industries such as healthcare, finance, and precision medicine are leveraging AI for decision-making, autonomous systems, and real-time data processing. AI infrastructure requires high computational demands, and cloud computing provides scalable storage solutions and cost-efficiency. Networking solutions offer high-bandwidth and low-latency for data transfer, ensuring data residency and data security.Data architecture includes databases, data warehouses, data lakes, in-memory databases, and caching mechanisms. Data preparation and resource utilization are crucial for model inference, data reconciliation, data classification, data visualization, and model validation. AI model production and data preprocessing are essential for continuous improvement and competitive advantage. AI accelerators, AI workflows, and data ingestion further enhance the capabilities of AI infrastructure. The market's growth is driven by the increasing need for cost-efficiency, integration, and modular systems.

    Get a glance at the Artificial Intelligence (AI) Infrastructure Industry report of share of various segments Request Free Sample

    The Processor segment was valued at USD 3.76 billion in 2018 and showed a gradual increase during the forecast period.

    Regional Analysis

    North America is estimated to contribute 49% to the growth of the global market during the forecast period.
    

    Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

    For more insights on the market share of various regions, Req

  15. D

    Department of Transportation Inventory of Artificial Intelligence Use Cases

    • data.transportation.gov
    • data.virginia.gov
    • +1more
    application/rdfxml +5
    Updated Apr 11, 2024
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    US Department of Transportation (2024). Department of Transportation Inventory of Artificial Intelligence Use Cases [Dataset]. https://data.transportation.gov/Administrative/Department-of-Transportation-Inventory-of-Artifici/anj8-k6f5
    Explore at:
    csv, tsv, application/rdfxml, json, xml, application/rssxmlAvailable download formats
    Dataset updated
    Apr 11, 2024
    Dataset authored and provided by
    US Department of Transportation
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Description

    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

  16. d

    AI Training Data | US Transcription Data| Unique Consumer Sentiment Data:...

    • datarade.ai
    Updated Jan 13, 2025
    + more versions
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    WiserBrand.com (2025). AI Training Data | US Transcription Data| Unique Consumer Sentiment Data: Transcription of the calls to the companies [Dataset]. https://datarade.ai/data-products/wiserbrand-ai-training-data-us-transcription-data-unique-wiserbrand-com
    Explore at:
    .csv, .xls, .txt, .jsonAvailable download formats
    Dataset updated
    Jan 13, 2025
    Dataset provided by
    WiserBrand.com
    Area covered
    United States
    Description

    WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

    WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

    User ID and Firm Name: Identify and categorize calls by unique user IDs and company names. Call Duration: Analyze engagement levels through call lengths. Geographical Information: Detailed data on city, state, and country for regional analysis. Call Timing: Track peak interaction times with precise timestamps. Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues. Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

    Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data, Consumer Behavior Data, Consumer Sentiment Data, Consumer Review Data, AI Training Data, Textual Data, and Transcription Data applications.

    WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

    Cases:

    1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

    Enriching STT Models: The dataset includes a wide variety of real-world customer service calls with diverse accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

    Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

    Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

    Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

    1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

    Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

    Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

    Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as ...

  17. G

    Generative AI Market Report

    • archivemarketresearch.com
    doc, pdf, ppt
    Updated Jun 3, 2025
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    Archive Market Research (2025). Generative AI Market Report [Dataset]. https://www.archivemarketresearch.com/reports/generative-ai-market-5028
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Jun 3, 2025
    Dataset authored and provided by
    Archive Market Research
    License

    https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    global
    Variables measured
    Market Size
    Description

    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 .

  18. d

    TagX Data collection for AI/ ML training | LLM data | Data collection for AI...

    • datarade.ai
    .json, .csv, .xls
    Updated Jun 18, 2021
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    TagX (2021). TagX Data collection for AI/ ML training | LLM data | Data collection for AI development & model finetuning | Text, image, audio, and document data [Dataset]. https://datarade.ai/data-products/data-collection-and-capture-services-tagx
    Explore at:
    .json, .csv, .xlsAvailable download formats
    Dataset updated
    Jun 18, 2021
    Dataset authored and provided by
    TagX
    Area covered
    Belize, Colombia, Antigua and Barbuda, Russian Federation, Saudi Arabia, Benin, Djibouti, Qatar, Equatorial Guinea, Iceland
    Description

    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.

  19. Artificial Intelligence (AI) market size/revenue comparisons 2020-2030

    • statista.com
    Updated Dec 15, 2024
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    Bergur Thormundsson (2024). Artificial Intelligence (AI) market size/revenue comparisons 2020-2030 [Dataset]. https://www.statista.com/study/144944/artificial-intelligence-in-labour-and-productivity/
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Bergur Thormundsson
    Description

    The market for artificial intelligence (AI) is expected to show significant growth in the coming decade, according to a variety of sources. According to Statista data, the AI market size is projected to rise from 241.8 billion U.S. dollars in 2023 to almost 740 billion U.S. dollars in 2030, accounting for a compound annual growth rate of 17.3%. Meanwhile, according to  Next Move Strategy Consulting, its value of approximately 208 billion U.S. dollars in 2023 is expected to grow ninefold by 2030, reaching around 1.85 trillion U.S. dollars. Indeed, the AI market covers a vast number of industries, including healthcare, education, finance, media and marketing. The rate of adoption and deployment of the technology is becoming more prolific worldwide. Chatbots, image-generating AI, and mobile applications are all among the major trends that will enhance AI in the coming years.

    AI demands data

    Data management remains the most difficult task of AI-related infrastructure. This challenge takes many forms for AI companies. Some require more specific data, while others have difficulty maintaining and organizing the data their enterprise already possesses. Large international bodies like the EU, the US, and China all have limitations on how much data can be stored outside their borders. Together these bodies pose significant challenges to data-hungry AI companies.

    AI could boost productivity growth

    Both in productivity and labor changes, the U.S. is likely to be heavily impacted by the adoption of AI. This impact need not be purely negative. Labor rotation, if handled correctly, can swiftly move workers to more productive and value-added industries rather than simple manual labor ones. In turn, these industry shifts will lead to a more productive economy. Indeed, AI could boost U.S. labor productivity growth over a 10-year period. This, of course, depends on a variety of factors, such as how powerful the next generation of AI is, the difficulty of tasks it will be able to perform, and the number of workers displaced.

  20. Artificial Intelligence Market Research Report 2033

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Jun 28, 2025
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    Growth Market Reports (2025). Artificial Intelligence Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/artificial-intelligence-market-global-industry-analysis
    Explore at:
    pptx, pdf, csvAvailable download formats
    Dataset updated
    Jun 28, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    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

    &l

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Factori (2019). Factori Machine Learning (ML) Data | 247 Countries Coverage | 5.2 B Event per Day [Dataset]. https://datarade.ai/data-products/factori-ai-ml-training-data-web-data-machine-learning-d-factori

Factori Machine Learning (ML) Data | 247 Countries Coverage | 5.2 B Event per Day

Explore at:
.csvAvailable download formats
Dataset updated
Oct 1, 2019
Dataset authored and provided by
Factori
Area covered
Cameroon, Sweden, Egypt, Faroe Islands, Turks and Caicos Islands, Japan, Austria, Uzbekistan, Taiwan, Palestine
Description

Factori's AI & ML training data is thoroughly tested and reviewed to ensure that what you receive on your end is of the best quality.

Integrate the comprehensive AI & ML training data provided by Grepsr and develop a superior AI & ML model.

Whether you're training algorithms for natural language processing, sentiment analysis, or any other AI application, we can deliver comprehensive datasets tailored to fuel your machine learning initiatives.

Enhanced Data Quality: We have rigorous data validation processes and also conduct quality assurance checks to guarantee the integrity and reliability of the training data for you to develop the AI & ML models.

Gain a competitive edge, drive innovation, and unlock new opportunities by leveraging the power of tailored Artificial Intelligence and Machine Learning training data with Factori.

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.

Web Data Reach: Our reach data represents the total number of data counts available within various categories and comprises attributes such as Country, Anonymous ID, IP addresses, Search Query, and so on.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Data Attributes: Anonymous_id IDType Timestamp Estid Ip userAgent browserFamily deviceType Os Url_metadata_canonical_url Url_metadata_raw_query_params refDomain mappedEvent Channel searchQuery Ttd_id Adnxs_id Keywords Categories Entities Concepts

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