In 2022, the global total corporate investment in artificial intelligence (AI) reached almost ** billion U.S. dollars, a slight decrease from the previous year. In 2018, the yearly investment in AI saw a slight downturn, but that was only temporary. Private investments account for a bulk of total AI corporate investment. AI investment has increased more than ******* since 2016, a staggering growth in any market. It is a testament to the importance of the development of AI around the world. What is Artificial Intelligence (AI)? Artificial intelligence, once the subject of people’s imaginations and the main plot of science fiction movies for decades, is no longer a piece of fiction, but rather commonplace in people’s daily lives whether they realize it or not. AI refers to the ability of a computer or machine to imitate the capacities of the human brain, which often learns from previous experiences to understand and respond to language, decisions, and problems. These AI capabilities, such as computer vision and conversational interfaces, have become embedded throughout various industries’ standard business processes. AI investment and startups The global AI market, valued at ***** billion U.S. dollars as of 2023, continues to grow driven by the influx of investments it receives. This is a rapidly growing market, looking to expand from billions to trillions of U.S. dollars in market size in the coming years. From 2020 to 2022, investment in startups globally, and in particular AI startups, increased by **** billion U.S. dollars, nearly double its previous investments, with much of it coming from private capital from U.S. companies. The most recent top-funded AI businesses are all machine learning and chatbot companies, focusing on human interface with machines.

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...

1. The horticulture industry places a high value on money plants because of their hardiness and aesthetic attractiveness. Nevertheless, several illnesses might have a substantial negative influence on their well-being and output, making cultivation difficult. For a therapy to be effective, leaf diseases must be accurately and quickly identified. High-resolution photos of money plant leaves were taken at the Savar demonstration site in Dhaka, Bangladesh, and are included in this dataset. The photos are divided into three different classes: Manganese Toxicity (596 images), Bacterial Wilt Disease (576 images), and Healthy (700) images. These classes represent both damaged and healthy leaves. The dataset has 2100 images in total. Comprehensive comments that describe the nature and severity of the condition are included with every photograph. For accurate and trustworthy model training and validation, this data is essential. The information also contains metadata that records the location and surrounding circumstances at the time the photograph was taken. Understanding the environmental factors influencing the prevalence of disease and enhancing the accuracy of predictive models require this contextual information.
2. At the moment, there are a lot of potential deep learning and computer vision techniques to handle these kinds of categorization and detection problems.
3. To create deep learning techniques, an extensive money plant disease dataset is provided. The subject matter expert from an agricultural institute collaborated with us to construct the classifications for this dataset.
4. From the Savar demonstration place in Dhaka, Bangladesh, a total of 1872 images depicting Bacterial Wilt Disease (576), Healthy (700), and Manganese Toxicity (596) were collected. Then, using methods like flipping, width shifting, height shifting, brightening, rotating, shearing, and zooming, 6000 augmented images are made from these original photos to increase the quantity of data sets.

AI-Based Fraud Detection Tools Market Outlook

The global AI-based fraud detection tools market size was valued at approximately USD 6.5 billion in 2023 and is projected to reach USD 22.8 billion by 2032, growing at a robust CAGR of 15.1% during the forecast period. The significant growth factors driving this market include the increasing sophistication of fraudulent activities, the growing adoption of AI and machine learning technologies in various sectors, and the heightened demand for real-time fraud detection solutions.

One of the primary growth factors for the AI-based fraud detection tools market is the rising complexity of fraudulent activities. In today's digital age, fraudsters are employing increasingly sophisticated techniques to breach security systems, making traditional detection methods inadequate. AI-based solutions, which leverage advanced algorithms and machine learning, are capable of analyzing large volumes of data to identify patterns and anomalies indicative of fraud. This capability is crucial for organizations seeking to protect their assets and maintain customer trust in an environment where cyber threats are continually evolving.

Another significant growth driver is the widespread adoption of AI and machine learning technologies across various industries. Businesses are recognizing the potential of these technologies to enhance their fraud detection capabilities, leading to increased investments in AI-driven solutions. The banking and financial services sector, in particular, has been at the forefront of adopting AI-based fraud detection tools to combat financial crimes such as identity theft, credit card fraud, and money laundering. Furthermore, the retail and e-commerce sectors are increasingly implementing these tools to safeguard against fraudulent transactions and account takeovers.

The growing demand for real-time fraud detection solutions is also propelling the market forward. Traditional fraud detection systems often rely on rule-based approaches that can be slow and reactive, allowing fraudulent activities to go undetected until significant damage has been done. In contrast, AI-based solutions can process and analyze data in real-time, enabling organizations to identify and respond to threats rapidly. This real-time capability is essential for minimizing losses and mitigating risks, particularly in sectors where the speed of transactions is critical, such as online retail and financial services.

Regionally, North America currently dominates the AI-based fraud detection tools market, owing to the high adoption rate of advanced technologies and the presence of major industry players. However, other regions like Asia Pacific and Europe are also experiencing significant growth. Asia Pacific, in particular, is expected to exhibit the highest CAGR during the forecast period, driven by the increasing digitization of economies, rising internet penetration, and the growing awareness of cybersecurity threats. Europe is also witnessing substantial growth due to stringent regulatory requirements and the increasing focus on data privacy and security.

Component Analysis

The AI-based fraud detection tools market can be segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share during the forecast period. This dominance can be attributed to the continuous advancements in AI algorithms and machine learning models, which enhance the accuracy and efficiency of fraud detection systems. Furthermore, the software solutions are designed to be scalable and easily integrated into existing systems, making them an attractive option for organizations of all sizes.

Hardware components, though not as dominant as software, play a crucial role in the deployment of AI-based fraud detection systems. High-performance computing hardware, including GPUs and specialized AI processors, are essential for handling the large datasets and complex computations required for real-time fraud detection. As the demand for more powerful and efficient hardware grows, this segment is expected to see steady growth, particularly in large enterprises that require robust infrastructure to support their AI initiatives.

The services segment, encompassing consulting, integration, and maintenance services, is also poised for significant growth. Organizations often lack the in-house expertise required to develop and implement AI-based fraud detection systems, leading to an increased reliance on external service providers. These services help organizations to customize and opti

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

Reasons business or organization does not plan to use artificial intelligence (AI) in producing goods or delivering services over the next 12 months, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2025.

Quarterly data on foreign portfolio investment in Canadian bonds and Canadian money market instruments by sector and geographic region.

Cash-and-Short-Term-Investments Time Series for Taskus Inc. TaskUs, Inc. provides outsourced digital services for companies in Philippines, the United States, India, and internationally. The company offers digital customer experience that consists of omni-channel customer care services primarily delivered through non-voice digital channels; and other solutions, including learning experience and customer care services, new product or market launches, and customer acquisition solutions. It also provides trust and safety solutions, such as monitoring, reviewing and managing user and advertiser-generated content on online platforms to ensure it complies with community guidelines, legal regulations, platform specific policies, risk management, compliance, identity management and fraud; and artificial intelligence (AI) solutions that consist of data labeling, annotation, context relevance, and transcription services for training and tuning machine learning algorithms that enables to develop AI systems. It serves clients in various industry segments comprising social media, e-commerce, gaming, streaming media, food delivery and ride sharing, technology, financial services, and healthcare. The company was formerly known as TU TopCo, Inc. and changed its name to TaskUs, Inc. in December 2020. TaskUs, Inc. was founded in 2008 and is headquartered in New Braunfels, Texas.

Analysis of ‘Private sector credit flow: loans by sectors, non-consolidated - million units of national currency’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://data.europa.eu/data/datasets/5a5msphjfegxkne25clrg on 29 August 2021.

--- Dataset description provided by original source is as follows ---

The table presents the net flow of loans (F.4) for the sectors Non-Financial corporations (S.11), Households (S.14) and Non-Profit institutions serving households (S.15). The debt securities are negotiable financial instruments serving as evidence of debt. Data are presented in non-consolidated terms, i.e. data take into account transactions within the same sector and expressed in Million units of national currency. Definitions regarding sectors and instruments are based on the ESA 2010.

--- Original source retains full ownership of the source dataset ---

Cash-and-Equivalents Time Series for Capitalonline Data Service Co Ltd. Capitalonline Data Service Co., Ltd. provides cloud hosts and related services in China, the Americas, Europe, and the Asia Pacific. It offers cloud computing, artificial intelligence, and other products and services. The company also provides cloud and network integrated products, including elastic computing, global network, IDC services, data processing, and security, as well as storage, AWS cloud, database, safety, and big data and enterprise applications. In addition, it offers solutions, such as cloud connectivity, gaming, migration, and XR; e-commerce; mobile apps; online video; real estate; MaaS; digital twin; online education; CDS intelligent dispatch management platform; online games; and internet finance. The company provides its services to digital twins, artificial intelligence, industrial Internet, Internet of Vehicles, big data, education, finance, video, e-commerce, games, medical care, government, and other industries. The company was founded in 2005 and is headquartered in Beijing, China.

Change-In-Cash Time Series for Intapp Inc. Intapp, Inc., through its subsidiary, Integration Appliance, Inc., provides AI-powered solutions in the United States, the United Kingdom, and internationally. Its solutions includes DealCloud that manages client relationships, prospective clients and investments, current engagements, as well as provides customer relationship management, deal management, experience management, and relationship intelligence solutions. The company also offers compliance products that help firms thoroughly evaluate new business, onboard clients quickly, and monitor relationships for risk throughout their business lifecycle; time solutions provides AI-enabled software solutions includes time capture, enhances billing, and facilitates compliance with client requirements. In addition, it provides collaboration products offers intelligent client-centric teamwork with Microsoft 365, Teams, and SharePoint; unified system for managing emails, documents, chats, and tasks; and Assist, an AI-driven transformation that integrate advanced machine learning and natural language processing into Intapp products, such as Intapp DealCloud and Intapp Terms, as well as streamlines critical workflows, enhances decision-making, and delivers measurable results. Further, the company operates technology platforms, such as cloud-based architecture, low-code configurability and personalized UX, applied AI, and industry-specific data architecture. It serves private capital, investment banking, legal, accounting, and consulting firms, and real assets. The company was formerly known as LegalApp Holdings, Inc. and changed its name to Intapp, Inc. in February 2021. Intapp, Inc. was founded in 2000 and is headquartered in Palo Alto, California.

Free-Cash-Flow-To-The-Firm Time Series for Applied Digital Corporation. Applied Digital Corporation designs, develops, and operates digital infrastructure solutions to high-performance computing (HPC) and artificial intelligence industries in North America. It operates through: Data Center Hosting Business, and HPC Hosting Business. The company offers infrastructure services to crypto mining customers; and GPU computing solutions for critical workloads related to AI, machine learning, and other HPC tasks. It also engages in the designing, constructing, and managing of data centers to support HPC applications. The company was formerly known as Applied Blockchain, Inc. and changed its name to Applied Digital Corporation in November 2022. Applied Digital Corporation is based in Dallas, Texas.

Reasons business or organization does not accept certain payment methods, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, third quarter of 2023.

Free-Cash-Flow-To-The-Firm Time Series for Salesforce.com Inc. Salesforce, Inc. provides customer relationship management (CRM) technology that connects companies and customers together worldwide. The company offers Agentforce, an agentic layer of the salesforce platform; Data Cloud, a data engine; Industries AI for creating industry-specific AI agents with Agentforce ; Salesforce Starter, a suite of solution for small and medium-size business; Slack, a workplace communication and productivity platform; Tableau, an end-to-end analytics solution for range of enterprise use cases and intelligent analytics with AI models, spot trends, predict outcomes, timely recommendations, and take action from any device; and integration and analytics solutions. It also provides marketing platform; commerce services, which empowers shopping experience across various customer touchpoint; and field service solution that enables companies to connect service agents, dispatchers, and mobile employees through one centralized platform to schedule and dispatch work, as well as track and manage jobs. Salesforce, Inc. was incorporated in 1999 and is headquartered in San Francisco, California.

Total-Cash-From-Operating-Activities Time Series for Yunding Technology Co Ltd. Yunding Technology Co.,Ltd. engages smart mining business in China. The company offers smart power and new energy products, including power production management system, photovoltaic intelligent operation and maintenance platform system, engineering project management and control system, etc. It also offers intelligent washing and sorting systems, such as coal washing automation system integration, coal preparation plant intelligent solutions, intelligent online ash detection systems, and industrial and mining intelligent supporting equipments. In addition, the company provides smart mining products, such as intelligent software; intelligent internet of things, such as Network communication; and intelligent positioning products, such as personnel precise positioning and paratransit systems. Further, it engages in ERP implementation and operation and maintenance services, including digital innovation services, packaged software consulting and implementation, development management solutions and services, consulting, technical implementation, operations, and maintenance. Additionally, it engages in industrial internet platform business that provides Dingyun Industrial Internet Platform, an artificial intelligence (AI) application platform that offers cloud-edge collaboration, data collection, data fusion, digital intelligence collaboration, and application development; AI service platform, a one-stop model training workflow, flexible manufacturing of artificial intelligence models, process control from data annotation, model training, model release, model deployment, and business scenario application; and comprehensive management and control platform for production safety technology. The company also engages in coal preparation plant automation and coal gasification technology promotion business. Yunding Technology Co.,Ltd. was founded in 1993 and is based in Jinan, China.

Change-In-Cash Time Series for Lanner Electronics. Lanner Electronics Inc., together with its subsidiaries, manufactures and sells Internet and communication equipment in the United States, Europe, China, Israel, Canada, and internationally. The company offers computer peripheral equipment, computer software design and development services, and related information processing trade services. It also provides edge AI appliances, including AI starter kits, as well as deep learning accelerators, inference appliances, and servers; and network appliances, such as Wi-Fi access points, x86 rackmount and desktop network appliances, modules and acceleration cards, and wide temperature network and industrial security appliances. In addition, the company offers telecom datacenter appliances comprising cloud storage platforms, hyper-converged appliances, and modules and blades, as well as open RAN, HybridTCA, and video transport platforms; and intelligent edge appliances, including DIN-Rail industrial gateways, embedded and intelligent video platforms, rackmount industrial computers, rugged wireless and vehicle gateways, and vehicle and rail computers. Further, it provides extension modules, such as POE NIC, 100G+ NIC, 10/25/40G NIC, radio, storage NIC, acceleration, and 1G NIC modules, as well as industrial GbE PCIe cards and smart NIC. Additionally, the company offers SDN and NFV, edge AI, and industrial IoT solutions, as well as intelligent transits systems, including onboard fleet monitoring solutions and intelligent public transit surveillances; and LEAP virtual lab, design and manufacturing, and technology building block services. It offers its products for various applications comprising network computing, telecommunication, transportation, power and energy, industrial automation, intelligent systems, and SD-WAN. Lanner Electronics Inc. was founded in 1986 and is headquartered in New Taipei City, Taiwan.

End-Period-Cash-Flow Time Series for AAEON Technology Inc. AAEON Technology Inc. designs, manufactures, and sells industrial computers and peripherals. The company offers embedded boards, such as embedded single board computers, computer-on-modules, industrial motherboards, smart display modules, and RISC single board computers; and computing systems, including fanless and in-vehicle embedded box PCs, turn-key chassis solutions, and industrial chassis. It also provides rugged tablet computers and accessories; UP boards, systems, and edge computing kit; artificial intelligence (AI) Edge solutions and modules; and network appliances comprising desktop and rackmount network appliances, industrial cyber security appliances, and network interface modules. In addition, the company offers IoT gateway boards and protocol expansion solutions, edge robot processor, and server board and system. Its products are used in smart city, safety and security, smart retail and manufacturing, AI computing platform, network appliance, software defined wide area network, intelligent vending machine, vision system solution, and smart office/wellness sensor applications. The company was founded in 1992 and is headquartered in New Taipei City, Taiwan.

Cash-Flow-Per-Share Time Series for Haier Smart Home Co Ltd. Haier Smart Home Co., Ltd. engages in the research, development, production, and sales of smart home appliances. The company provides refrigerators/freezers, washing machines, dryer, air conditioners, water heaters, kitchen and small appliances, and smart home scene solutions. It offers logistics services; distribution of home appliance business; manufacture and sale of plastic parts, precise plastics, plastic powder and sheet, metal plate, coatings, electronics and hardware products; mold and technological equipment; health-related home appliance; dish washing machine and gas stove; network engineering technology; industrial design and prototype production; development, promotion and transfer of technology; wholesale and retail of medical facility and equipment; development and sales of software and information product; and entrepreneurship investment and consulting services. In addition, the company engages in the sale of digital products, international freight forwarding; sanitary ware; software development; consulting management services; promotion of technological development; technical services; IoT technology and application services; leasing services; scientific research and technology development service; furniture customization; residential interior decoration, professional construction operation, and equipment installation. Further, it offers urban distribution and transportation services; import and export of goods, technology, and food business; big data, AI, and AR operations; professional intermediary activities; waste treatment; integrated service of AI industry application system and technology consulting; production certification service; professional cleaning and sale of food and daily necessities; energy saving technology; and advertisement design. The company was formerly known as Qingdao Haier Co., Ltd. and changed its name to Haier Smart Home Co., Ltd. in June 2019. The company was founded in 1984 and is headquartered in Qingdao, China.

Other-Non-Cash-Items Time Series for ADLINK Technology Inc. Adlink Technology Inc. manufactures and sells hardware, software, and peripheral devices of industrial computers in the Asia Pacific, Mainland China, the United States, and Europe. The company offers computer-on-modules; GPU solutions, including embedded MXM GPU modules, Intel Arc MXM GPU modules, graphic card, and NVIDIA professional graphics solutions; Rugged computing solutions, comprising compactPCI and compactPCI serial, PC104, VPX, rugged embedded computers, and railway and military rugged solutions; autonomous driving and AI-ADAS solutions; and design and manufacturing services. It also provides industrial edge and MEC servers, and network security appliance; industrial display systems and panel PCs; MicroRAN solutions; edge computing platforms, which include industrial PCs, motherboards, SBCs, embedded computers and IoT gateway, edge AI platforms, AI smart cameras, robotic controllers, and industrial solid state drives; and gaming platforms and monitors. In addition, the company offers healthcare computing and monitors; automation and control solutions; ADLINK DDS, an intelligent data sharing platform; EdgeGO device management software; and Zenoh, the zero overhead pub/sub, store/query and compute platform. It serves automotive, defense and aviation, healthcare, industrial automation, gaming, railway, retail and logistics, semiconductor, smart city, networking and communication, test and measurement, and robotics industries. Adlink Technology Inc. was incorporated in 1995 and is headquartered in Taoyuan City, Taiwan.