AI Training Dataset Market

According to a research report "AI Training Dataset Market by Dataset Creation (Data Collection, Data Annotation, Synthetic Data Generation), Dataset Selling (Off-the-Shelf Datasets, Dataset Marketplaces), Data Modality (Text, Image, Video, Audio, Multimodal) - Global Forecast to 2029" published by MarketsandMarkets, the market for AI Training Dataset is slated to expand from USD 2.82 billion in 2024 to USD 9.58 billion by the year 2029 at a robust CAGR of 27.7% over the forecast period

Browse 487 market data Tables and 66 Figures spread through 446 Pages and in-depth TOC on "AI Training Dataset Market by Dataset Creation (Data Collection, Data Annotation, Synthetic Data Generation), Dataset Selling (Off-the-Shelf Datasets, Dataset Marketplaces), Data Modality (Text, Image, Video, Audio, Multimodal) - Global Forecast to 2029"
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The market for AI training datasets has gained substantial traction, with the major catalyst being the need for fair and unbiased datasets. Enterprises are gradually realizing the implications of bias within the dataset. Such bias was highlighted in the case of the Apple Card, where women were given lower credit limits than men due to biased training data embedded in the credit disbursal algorithms. Large language models have also been criticized for making negative stereotypes, such as when OpenAI’s GPT-3 unintentionally linked objectionable words to certain ethnic groups. These cases stress the need for curating well-balanced training datasets that adequately capture real life scenarios; and are inclusive as well. Other factors helping the market growth include the rise of synthetic data to address privacy concerns and scarcity issues, allowing industries like healthcare and autonomous vehicles to simulate rare scenarios. Other pivotal market trends include the progressively increasing use of multimodal datasets, to power virtual assistants and smart gadgets that require the simultaneous processing of text, images and audio.

By offering, dataset creation segment will account for largest market share in 2024 owing to high demand for accurately labelled datasets.

The market for data labeling & annotation software is expected to hold major market share in 2024, spurred along by the rising need for accurate and precisely labelled data. One of the main factors for growth is the rising demand for context-specific annotations that go beyond basic labeling. Companies like Tempus Labs are using intricately labeled genomic and clinical data to develop precision medicine AI tools, requiring highly detailed and specialized annotations from medical experts. Furthermore, with the introduction of AI-powered annotation automation tools such as SuperAnnotate, the AI annotation is combined with human annotators, creating a human-in-the-loop (HITL) system that enhances workflow efficiency. This has become a popular trend as organizations want to reduce the amount of manual work while maintaining good standards. For example, Aptiv is leveraging such HITL datasets for training advanced driver-assistance systems (ADAS). Another major factor is the progressive increase in the adoption of multimodal data, which require highly accurate and robustly annotated dataset across various modalities.

Rising consumption of high-quality datasets to develop domain-specific AI models will push software & technology providers as the fastest growing end user segment during the forecast period

The software and technology providers segment is experiencing the fastest growth in the AI training dataset market, driven by increasing demand for scalable and high-quality dataset creation solutions. These providers, especially cloud hyperscalers like AWS and Google Cloud, are leveraging massive datasets to enhance AI offerings like voice recognition, computer vision, and natural language processing. Microsoft Azure, for instance, has launched several services like Azure Machine Learning that take advantage of large amounts of data to train advanced AI models. Foundation models providers, such as Cohere and Anthropic, are also investing a lot of resources into the procurement of datasets in order to train and custom design LLMs. Furthermore, IT services companies are developing end-to-end data pipelines for their customers, allowing them to scale AI applications with ethically sourced and unbiased training datasets. The segment’s robust expansion is also aided by the growing use of industry specific datasets for niche applications like AI in cyber security and supply chain analytics.

North America is set to hold the largest market share in 2024, fueled by a strong regulatory environment and increasing investments in responsible AI deployment

North America has emerged as the largest regional market for AI training dataset, owing to hefty R&D investments being poured into AI. As reported in the 2022 US budget, the federal AI spending of the US government was greater than USD 3.3 billion dollars, which created a demand for quality training datasets. The region’s strong focus on advancing large-scale AI models like GPT-4 by OpenAI and DeepMind’s AlphaFold also showcases the requirement for multimodal and high-quality training datasets to develop such models. Also, the existence of cloud hyperscalers like AWS, Microsoft Azure, and Google Cloud has sped up the provision of scalable AI solutions, including data annotation and management, as part of their cloud services. In Canada, companies like Element AI (acquired by ServiceNow) are creating sophisticated AI models for sectors like finance and logistics, driving the need for reliable datasets to ensure precision and effectiveness.

This trend is also assisted by the North American regulatory landscape, which favors responsible artificial intelligence practices, increasing the market demand for data sets that are both transparent and free from bias. A similar trend is reflected in California’s Automated Decision Systems Accountability Act (AB-13) which seeks to ensure that AI systems are fair and accountable.

The major players in the AI training dataset market include Scale AI (US), Appen (Australia), Lionbridge Technologies (US), AWS (US), and Sama (US), along with SMEs and startups such as Snorkel AI (US), V7 Labs (UK), Alegion (US), Toloka AI (US), and iMerit (US).

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