市場調查報告書
商品編碼
1467951
人工智慧訓練資料集市場:按類型、最終用戶分類 - 2024-2030 年全球預測AI Training Dataset Market by Type (Audio, Image/Video, Text), End-User (Automotive, Banking, Financial Services & Insurance (BFSI), Government) - Global Forecast 2024-2030 |
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人工智慧訓練資料集市場規模預計2023年為17.1億美元,2024年達到21.2億美元,預計2030年將達到88.3億美元,複合年成長率為26.41%。
人工智慧 (AI) 訓練資料集是用於訓練 AI 模型處理資訊、做出預測並學習執行特定任務的綜合資料集,而無需明確程式設計。 AI 訓練資料集用於開發用於預測分析、醫學影像識別、語音辨識系統、機器學習 (ML) 和人工智慧 (AI) 解決方案的 AI 模型。因此,這些資料集的最終用戶包括開發人工智慧演算法的科技公司、致力於智慧設備和解決方案的新興企業以及致力於尖端人工智慧技術的研究機構。由於人工智慧技術在製造和醫療保健等各行業的普及,以及對人工智慧技術的大量投資,對人工智慧訓練資料集的需求出現了。此外,政府針對工業 4.0、智慧工廠和智慧建築的舉措也為人工智慧訓練資料集的成長提供了新的途徑。然而,訓練資料缺乏品質和多樣性可能會導致人工智慧效率低下和模型出現偏差。此外,隱私問題以及建立、管理和更新人工智慧訓練資料集所涉及的技術複雜性也是主要限制因素。然而,主要參與者正在專注於改進來自不同來源的資料集的聚合,以代表不同的人口統計數據,這將有助於消除偏見並促進有效的資料標記,並可以投入精力開發匿名化技術。 KEYWORD 創新和研究可以致力於提高資料品質、代表性和可用性。
主要市場統計 | |
---|---|
基準年[2023] | 17.1億美元 |
預測年份 [2024] | 21.2億美元 |
預測年份 [2030] | 88.3億美元 |
複合年成長率(%) | 26.41% |
類型:採用基於文字的人工智慧訓練資料集進行各行業的文本分類和情感分析
近年來,由於 IT 行業擴大將文字資料集集用於語音辨識、文字分類和字幕生成等各種自動化流程,因此文字領域繼續佔據重要地位。 AI訓練資料集的文本分類被認為是對文字進行類別的智慧分類,並且透過使用機器學習(ML)來自動化這些任務,整個過程變得極其快速和高效。此外,音樂、語音、語音命令、多模態心線 (MELD) 和環境音訊資料集集等音訊資料集也廣泛可用。基於語音的人工智慧訓練資料集可以提高工作效率,允許使用者口述文件、電子郵件回覆和其他文本,而無需手動將資訊輸入機器。然而,獲取基於語音的人工智慧訓練資料集的成本相對較高,具體取決於資料集的大小。
電腦視覺系統的圖像和視訊資料收集有幾個優點,包括圖像特定的儲存庫、根據您的要求標記圖像的能力以及存取歷史資料。行為識別已成為研究界關注的主要領域,因為視訊搜尋、視訊字幕和視訊問答等許多應用都可以從改進的建模中受益。視訊資料集在解決防止人類定位的各種挑戰方面發揮著重要作用,例如密集對應、深度、運動、身體截面和遮蔽資訊。
最終用戶:擴大資訊科技在全球的足跡,需要部署先進的人工智慧訓練資料集
資訊科技可以透過支援群眾外包、資料分析和虛擬助理等各種解決方案,為企業帶來巨大好處。醫療保健領域的人工智慧在生活方式和健康管理、診斷、虛擬助理和穿戴式裝置等領域提供了多種機會。此外,人工智慧將應用於支援語音的症狀檢查器,以改善組織工作流程。這些人工智慧應用程式需要大量資料集才能提供準確的結果。此外,基於汽車應用的人工智慧和深度學習模型提供了許多有價值的見解和分析,以準確檢測駕駛員行為。採用人工智慧感測器和系統將有助於檢測突發行為並提供警告訊號以避免事故。
在 BFSI,基於 NLP 的聊天機器人和人工智慧訓練資料集支援的語音機器人可以回答客戶有關每月費用、貸款合格和負擔得起的保險計劃的問題,為消費者提供 24 小時不間斷的服務。此外,基於人工智慧的訓練資料集可以分析產品目錄資料並預測未來的產品需求,使零售商和電子零售商能夠就存量基準做出明智的決策,並且可以避免產品庫存過多或庫存不足。在政府部門,人工智慧訓練資料集幫助識別逃稅模式,篩選基礎設施資料以進行橋樑檢查,篩選健康和社會服務資料以優先考慮兒童福利和支持,並檢測感染疾病。 ETC。它使世界各地的政府能夠更有效地業務,改善資料結果,並降低政府業務和程序的成本。
區域洞察
美洲地區,特別是美國和加拿大,其特點是存在部署先進人工智慧訓練資料集集的成熟科技公司。在醫療保健、金融、網路安全和電子商務等領域,人工智慧訓練資料集正在為高級演算法訓練提供支持,以支援預測分析、客戶行為分析和詐騙檢測等任務。在歐盟國家,人們越來越關注用戶線上隱私和資料保護,從而催生了以消費者資料權利為中心的創新解決方案和人工智慧培訓資料集。此外,人工智慧研究和開發舉措正在獲得政府和私營部門的大量投資。越來越多的科技新興企業和企業專注於提供基於人工智慧的數位服務,創造了對人工智慧訓練資料集的需求。中國和印度等許多國家的網路普及很高,提供了龐大的消費群和快速成長的數位服務需求。工業 4.0 措施和政府提高自動化程度的努力正在進一步加速人工智慧訓練資料集集的部署。
FPNV定位矩陣
FPNV定位矩陣對於評估AI訓練資料集市場至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市場佔有率分析
市場佔有率分析是一個綜合工具,可以對人工智慧訓練資料集市場供應商的現狀進行深入而詳細的研究。全面比較和分析供應商在整體收益、基本客群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該行業競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、分散主導地位和合併特徵等因素。詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,以獲得市場競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:提供有關新產品發布、開拓地區、最新發展和投資的詳細資訊。
4.競爭評估及資訊:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況、製造能力等進行綜合評估。
5. 產品開發與創新:提供對未來技術、研發活動和突破性產品開發的見解。
1.AI訓練資料集市場的市場規模和預測是多少?
2.在人工智慧訓練資料集市場的預測期內,有哪些產品、細分市場、應用程式和領域需要考慮投資?
3.AI訓練資料集市場的技術趨勢和法規結構是什麼?
4.AI訓練資料集市場主要廠商的市場佔有率是多少?
5.進入AI訓練資料集市場的合適型態和策略手段是什麼?
[198 Pages Report] The AI Training Dataset Market size was estimated at USD 1.71 billion in 2023 and expected to reach USD 2.12 billion in 2024, at a CAGR 26.41% to reach USD 8.83 billion by 2030.
An artificial intelligence (AI) training dataset is a comprehensive set of data used to train AI models to process information, make predictions, and learn to perform specific tasks without explicit programming. AI training datasets are used for the development of AI models utilized in predictive analytics, medical image recognition, voice and speech recognition systems, and machine learning (ML) and artificial intelligence (AI) enabled solutions. Consequently, the end users of these datasets are diverse, consisting of technology firms developing AI algorithms, startups working on smart devices and solutions, and research institutions involved in cutting-edge AI technologies. The proliferation of AI technologies in various industries, such as manufacturing and healthcare, and significant investment in AI technology has created the need for AI training datasets. Furthermore, government initiatives for Industry 4.0, smart factories, and smart buildings provide new avenues for the growth of AI training datasets. However, lacking quality and diversity in the training data can lead to inefficient AI and biased models. Furthermore, privacy issues and technical complexities involved in creating, managing, and updating AI training datasets pose significant limitations. However, major players focus on improving the aggregation of datasets from diverse sources to represent different demographics, which can help eliminate bias, and efforts could be invested in developing techniques for efficient data labeling and anonymization. Innovation and research in AI training datasets can be redirected toward improving data quality, representation, and usability.
KEY MARKET STATISTICS | |
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Base Year [2023] | USD 1.71 billion |
Estimated Year [2024] | USD 2.12 billion |
Forecast Year [2030] | USD 8.83 billion |
CAGR (%) | 26.41% |
Type: Adoption of text-based AI training datasets for text classification and sentiment analysis in various industries
The text segment has remained significant in recent years owing to the rising use of text datasets in the IT industry for diverse automation processes such as speech recognition, text classification, and caption generation. Text classification for AI training datasets is considered a smart classification of text into categories, and using machine learning (ML) to automate these tasks makes the entire process exceptionally fast and efficient. Moreover, audio datasets such as music, speech, speech, speech commands, multimodal emotion lines (MELD), and environmental audio datasets are widely available. The audio-based AI training datasets allow improved productivity, allowing users to dictate documents, email responses, and other text without manually inputting any information into a machine. However, the cost of acquiring audio-based AI training datasets is relatively high, depending on the size of the dataset.
Image or video data collection for computer vision systems has several benefits, including a unique image-specific repository, the ability to label images as per requirements, and access to historical data. Action recognition has become a major focus area for the research community as many applications can benefit from improved modeling, such as video retrieval, video captioning, and video question-answering. Video datasets play a critical role in addressing various difficulties in preventing human positioning, including dense correspondence, profundity, motion, body sectioning, and occlusion information.
End-user: Expansion of information technology hubs across the world necessitating deployment of advanced AI training dataset
Information technology offers significant benefits to companies by enhancing various solutions such as crowdsourcing, data analytics, and virtual assistants. AI in healthcare offers multiple opportunities in areas such as lifestyle and wellness management, diagnostics, virtual assistants, and wearables. In addition, AI finds applications in a voice-enabled symptom checker and improves organizational workflow. These AI applications require an extensive dataset to provide accurate results. Moreover, AI and deep learning models based on automotive applications offer many valuable insights and analytics to detect driver behavior accurately. The adoption of AI sensors and systems aids in detecting drivers' behavior and provides warning signals to avoid accidents.
In BFSI, AI training dataset-enabled NLP-based chatbots and speech bots can answer a customer's questions regarding monthly costs, loan eligibility, and inexpensive insurance plans, providing uninterrupted service to consumers around the clock. Furthermore, AI-based training datasets can analyze data from the product catalog and predict future demand for products, allowing retailers and e-tailers to make informed decisions about inventory levels and avoid overstocking or understocking products. In the government sector, AI training datasets help identify tax-evasion patterns, sort through infrastructure data to target bridge inspections or sift through health and social-service data to prioritize cases for child welfare and support or predict the spread of infectious diseases. They enable governments worldwide to perform more efficiently, improving data outcomes and decreasing costs in various government operations and procedures.
Regional Insights
The Americas region, particularly the U.S. and Canada, is characterized by the presence of established technological firms deploying advanced AI training datasets. In several sectors, including healthcare, finance, cybersecurity, and eCommerce, AI training datasets facilitate sophisticated algorithm training, propelling tasks such as predictive analytics, customer behavior analysis, and fraud detection. In EU nations, there is a heightened focus on user's online privacy and data protection, leading to innovative solutions and AI training datasets centered on consumer data rights. Additionally, AI research and development initiatives have observed substantial governmental and private sector investment. The growing number of technology startups and businesses focussed on providing AI-based digital services has created demand for AI training datasets. Many countries, such as China and India, offer a vast consumer base with increasing internet penetration, driving a burgeoning demand for digital services. Government initiatives aimed toward advancing Industry 4.0 initiatives and automation efforts have further fuelled the deployment of AI training datasets.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the AI Training Dataset Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the AI Training Dataset Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the AI Training Dataset Market, highlighting leading vendors and their innovative profiles. These include ADLINK Technology Inc., Alegion Inc., Amazon Web Services, Inc., Anolytics, Appen Limited, Atos SE, Automaton AI Infosystem Pvt. Ltd., Clarifai, Inc., Clickworker GmbH, Cogito Tech LLC, DataClap, DataRobot, Inc., Deep Vision Data by Kinetic Vision, Deeply, Inc., Google LLC by Alphabet, Inc., Gretel Labs, Inc., Huawei Technologies Co., Ltd., International Business Machines Corporation, Lionbridge Technologies, LLC, Meta Platforms, Inc., Microsoft Corporation, Mindtech Global Limited, Mostly AI Solutions MP GmbH, NVIDIA Corporation, Oracle Corporation, PIXTA Inc., Samasource Impact Sourcing, Inc., SAP SE, Scale AI, Inc., Siemens AG, Snorkel AI, Inc., Sony Group Corporation, SuperAnnotate AI, Inc., TagX, UniCourt Inc., and Wisepl Private Limited.
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the AI Training Dataset Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the AI Training Dataset Market?
3. What are the technology trends and regulatory frameworks in the AI Training Dataset Market?
4. What is the market share of the leading vendors in the AI Training Dataset Market?
5. Which modes and strategic moves are suitable for entering the AI Training Dataset Market?