AI訓練資料集市場：現況分析與預測（2024-2032）

AI Training Dataset Market: Current Analysis and Forecast (2024-2032)

出版日期: 2024年05月01日 | 出版商:

UnivDatos Market Insights Pvt Ltd | 英文 144 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

由於人工智慧技術在各行業應用的日益普及，人工智慧訓練資料集市場預計將以約21.5%的複合年增長率強勁成長。近年來，人工智慧 (AI) 經歷了前所未有的成長和進步，人工智慧驅動的應用和技術在各個行業中越來越受歡迎。人工智慧的快速擴張促使對高品質、多樣化和全面的人工智慧訓練資料集的需求激增，以支援這些先進系統。此外，醫療保健、金融、電子商務和交通等領域也越來越多地採用人工智慧技術，也是推動人工智慧訓練資料集需求的主要因素。隨著企業和組織尋求利用人工智慧的力量來增強營運、改進決策並提供個人化體驗，需要強大、可靠且多樣化的資料來訓練這些人工智慧模型，對資料集的需求正在迅速增加。此外，機器學習（ML）和深度學習（DL）演算法的日益普及和擴散是促使人工智慧訓練資料集需求激增的主要因素。這些先進技術依賴大量資料來訓練模型、學習模式並做出準確的預測。例如，在韓國，到 2022 年，客戶資料將成為訓練人工智慧 (AI) 模型的主要來源，約 70% 的受訪公司表示如此。此外，約 62% 的受訪者表示他們使用內部資料來訓練人工智慧模型。

依類型劃分，市場分為文字、音訊、圖像、視訊和其他（感測器和地理）。文字資料集是目前用於訓練各種人工智慧和機器學習模型最廣泛使用的資料集。由於互聯網、書籍、文章、社交媒體和其他各種來源提供了大量信息，文本數據在數位時代無處不在。文字資料集通常比其他資料類型（如音訊和視訊）更容易收集、儲存和處理。此外，文字資料可用於訓練各種人工智慧和機器學習模型，包括用於情緒分析、文字分類、語言生成和機器翻譯等任務的自然語言處理 (NLP) 模型。文字資料還可用於訓練 NLP 以外任務的模型，例如文件摘要、資訊檢索，甚至某些類型的圖像和影片分析任務。文字資料的多功能性使得能夠開發各種人工智慧和機器學習應用程序，從聊天機器人和虛擬助理到內容推薦系統和自動寫作工具。此外，與需要更強大的硬體和更大的計算資源（例如高解析度圖像和視訊）的其他資料類型相比，文字資料通常需要更少的計算工作量來處理。這使得基於文字的人工智慧和機器學習模型更易於開發和部署，尤其是在資源受限的設備和運算能力有限的場景中。這些因素正在推動環境的發展，並增加了對用於訓練各種人工智慧和機器學習模型的文字資料集的需求。

根據部署模式，市場分為雲端和本地。基於雲端的部署已成為訓練 AI 和 ML 模型最廣泛使用的方法，大多數組織都選擇這種方法。其主要原因是基於雲端的操作所帶來的靈活性和可擴展性。基於雲端的部署提供了無與倫比的可擴展性，使企業能夠根據需求的變化輕鬆增加或減少運算資源。這對於訓練複雜的人工智慧和機器學習模型尤其重要，因為這些模型通常需要大量的運算和儲存容量。此外，雲端服務供應商通常會大力投資最新的硬體和軟體技術，使企業能夠獲得先進的運算資源，例如強大的 GPU 和機器學習專用硬體。這使得公司能夠利用尖端技術，而無需進行大量的內部投資。此外，基於雲端的部署促進了遠端資料存取和協作，使分散式團隊能夠在人工智慧和機器學習專案上無縫協作。這對於團隊分佈在不同地點的組織或需要與外部合作夥伴和資料來源協作的組織尤其有利。這些發展和其他發展極大地促進了基於雲端的模型在各種人工智慧和機器學習任務訓練中的廣泛採用。

根據最終用戶行業，市場分為 IT/電信、零售/消費品、醫療保健、汽車、BFSI 和其他（政府/製造）。BFSI 產業在人工智慧採用方面處於領先地位。例如，根據教育科技Edtech公司 Great Learning 於 2023 年 9 月發佈的報告，印度的銀行、金融服務和保險 (BFSI) 行業佔數據科學和分析職位的三分之一以上。這一顯著增長歸因於人工智慧、機器學習和大數據分析等新興技術的日益使用。這些進步正在推動風險管理、詐欺偵測和客戶服務等領域的進步。該行業對人工智慧的快速接受是因為它是數據驅動的。BFSI 產業本質上是數據驅動的，處理大量的金融交易、客戶資訊和市場數據。事實證明，這些豐富的資料是有效訓練和部署人工智慧和機器學習 (ML) 模型的關鍵要素。此外，BFSI 領域的人工智慧解決方案已證明其能夠簡化從詐欺偵測和風險管理到個人化客戶服務和投資組合最佳化等流程。這顯著提高了營運效率並降低了成本。此外，在競爭激烈的 BFSI 環境中，提供無縫且個人化的客戶體驗已成為策略當務之急。人工智慧驅動的聊天機器人、對話式介面和預測分析使銀行和金融機構能夠更有效地預測和回應客戶需求。這些因素對 BFSI 領域採用人工智慧領域做出了重大貢獻。

為了更瞭解TLS 的市場採用情況，我們將市場分為北美（美國、加拿大、北美其他地區）、歐洲（德國、英國、法國、西班牙、義大利、歐洲其他地區）、亞太地區（中國、日本、印度）、澳洲）、其他亞太地區）以及世界其他地區。北美已成為人工智慧訓練資料集最大、成長最快的市場之一。美國擁有史丹佛大學、麻省理工學院和卡內基美隆大學等一些世界領先的研究型大學，並且在人工智慧和機器學習研究方面取得了重大進展。此外，Google、Microsoft、Amazon等知名科技公司在北美建立了最先進的人工智慧實驗室，進一步促進了該領域的創新和進步。此外，美國政府認識到人工智慧的戰略重要性，並透過國家人工智慧計畫等措施大力投資支持研究和開發。此外，北美科技巨頭正在積極投資開發和留住頂尖人工智慧和機器學習人才，創造一個自我強化的創新和成長循環。最後，北美，尤其是美國，擁有蓬勃發展的創投生態系統，已向人工智慧和機器學習新創公司和公司注入了數十億美元。矽谷、波士頓和紐約等主要科技中心的存在正在推動投資資本進入人工智慧/機器學習產業。例如，根據S&P Global Market Intelligence的數據，2023年對生成式人工智慧公司的投資將大幅增加，超過整體併購活動的下降幅度。私募股權公司在生成式人工智慧領域投資了 21.8 億美元，是去年投資總額的兩倍。資本激增之際，2023 年私募股權支持的併購交易在全產業範圍內下降。這些因素使北美成為人工智慧和機器學習產業的主導力量，促使對人工智慧訓練資料集服務的需求增加，以支援人工智慧產業前所未有的成長速度。

市場上營運的主要公司包括Google、Microsoft、 Amazon Web Services, Inc.、IBM、Oracle、Alegion AI, Inc.、TELUS International、Lionbridge Technologies、LLC、Samasource Impact Sourcing, Inc.、Appen Limited等。

AI training datasets are the foundational data used to train and develop machine learning and artificial intelligence models. These datasets consist of labeled examples that the AI models use to learn patterns and relationships and make accurate predictions. Datasets are collected from various sources such as databases, websites, articles, video transcripts, social media, and other relevant data sources. The goal is to gather a diverse and representative set of data. The raw data is carefully labeled and annotated to provide the AI model with accurate information from which to learn. This involves categorizing, tagging, and describing the data.

The AI Training Dataset Market is expected to grow at a strong CAGR of around 21.5%, owing to the growing proliferation of AI technology applications across various industries. Artificial Intelligence (AI) has witnessed unprecedented growth and advancements in recent years, with AI-powered applications and technologies becoming increasingly prevalent across various industries. This rapid expansion of AI has led to a corresponding surge in the demand for high-quality, diverse, and comprehensive AI training datasets to power these advanced systems. Furthermore, the growing adoption of AI-powered technologies across sectors such as healthcare, finance, e-commerce, and transportation has been a major driver of the demand for AI training datasets. As companies and organizations seek to leverage the power of AI to enhance their operations, improve decision-making, and deliver personalized experiences, the need for robust, reliable, and diverse datasets to train these AI models has skyrocketed. Additionally, the growing popularity and widespread adoption of machine learning (ML) and deep learning (DL) algorithms have been a significant factor in the surge of demand for AI training datasets. These advanced techniques rely on vast amounts of data to train their models, learn patterns, and make accurate predictions. For instance, in South Korea, customer data emerged as the primary information source for training artificial intelligence (AI) models in 2022, as stated by almost 70 percent of the surveyed companies. Furthermore, approximately 62 percent of the respondents indicated their utilization of internal data for training their AI models.

Based on type, the market is segmented into text, audio, image, video, and others (sensor and geo). Text datasets are the most widely used datasets for training various AI and ML models currently. Text data is ubiquitous in the digital age, with vast amounts of information available on the internet, in books, articles, social media, and various other sources. Text datasets are generally easier to collect, store, and process compared to other data types, such as audio or video. Furthermore, Text data can be used to train a wide range of AI and ML models, including natural language processing (NLP) models for tasks like sentiment analysis, text classification, language generation, and machine translation. Text data can also be used to train models for tasks beyond NLP, such as document summarization, information retrieval, and even some types of image and video analysis tasks. The versatility of text data allows for the development of a diverse range of AI and ML applications, from chatbots and virtual assistants to content recommendation systems and automated writing tools. Additionally, text data is generally less computationally intensive to process compared to other data types, such as high-resolution images or video, which require more powerful hardware and greater computational resources. This makes text-based AI and ML models more accessible and feasible to develop and deploy, especially on resource-constrained devices or in scenarios with limited computational power. Factors such as these are fostering a conducive environment, driving the surge in demand for text datasets for the training of various AI and ML models.

Based on deployment mode, the market is bifurcated into cloud and on-premise. Cloud-based deployment has emerged as the most widely used avenue for training AI and ML models, with a majority of organizations opting for this approach. Primarily driven by the flexibility and scalability that comes with cloud-based operation. Cloud-based deployment offers unparalleled scalability, allowing organizations to easily scale up or down their computing resources as per their changing needs. This is particularly crucial for training complex AI and ML models, which often require significant computational power and storage capacity. Furthermore, cloud service providers often invest heavily in the latest hardware and software technologies, ensuring that organizations have access to state-of-the-art computing resources, including powerful GPUs and specialized machine learning hardware. This allows organizations to leverage cutting-edge technologies without the need for significant in-house investments. Additionally, cloud-based deployment facilitates remote data access and collaboration, enabling distributed teams to work together on AI and ML projects seamlessly. This is particularly beneficial for organizations with geographically dispersed teams or those that need to collaborate with external partners or data sources. These developments, among others, have contributed substantially to the widespread adoption of cloud-based models for training various AI and ML operations.

Based on the end-user industry, the market is segmented into IT and telecommunication, retail and consumer goods, healthcare, automotive, BFSI, and others (government and manufacturing). The BFSI sector stands out as the frontrunner in AI adoption. For instance, according to the report released by Edtech company Great Learning in September 2023, the banking, financial services, and insurance (BFSI) sector in India accounted for more than one-third of data science and analytics jobs. This significant growth can be attributed to the increasing utilization of emerging technologies such as artificial intelligence, machine learning, and big data analytics. These advancements have particularly driven progress in areas like risk management, fraud detection, and customer service. This sector's rapid embrace of AI can be attributed to the industry's data-driven nature. The BFSI industry is inherently data-driven, dealing with vast amounts of financial transactions, customer information, and market data. This abundance of data has proven to be a crucial enabler for the effective training and deployment of AI and machine learning (ML) models. Furthermore, AI-powered solutions in the BFSI sector have demonstrated their ability to streamline various processes, from fraud detection and risk management to personalized customer service and investment portfolio optimization. This has led to significant improvements in operational efficiency and cost savings. Additionally, in the highly competitive BFSI landscape, delivering a seamless and personalized customer experience has become a strategic imperative. AI-driven chatbots, conversational interfaces, and predictive analytics have enabled banks and financial institutions to anticipate and cater to customer needs more effectively. Factors such as these have contributed significantly to the global adoption of AI within the BFSI sector.

For a better understanding of the market adoption of TLS, the market is analyzed based on its worldwide presence in countries such as North America (The U.S., Canada, and the Rest of North America), Europe (Germany, The U.K., France, Spain, Italy, Rest of Europe), Asia-Pacific (China, Japan, India, Australia, Rest of Asia-Pacific), Rest of World. North America has emerged as one of the largest and fastest-growing markets for AI training datasets. The United States is home to some of the world's leading research universities, such as Stanford, MIT, and Carnegie Mellon, which have made significant strides in AI and ML research. Furthermore, prominent tech companies, including Google, Microsoft, and Amazon, have established cutting-edge AI research labs in North America, further driving innovation and advancements in the field. Additionally, the U.S. government has recognized the strategic importance of AI and has invested heavily in supporting research and development through initiatives like the National Artificial Intelligence Initiative. Moreover, major tech companies in North America have been actively investing in training and retaining top AI and ML talent, creating a self-reinforcing cycle of innovation and growth. Lastly, North America, especially the U.S., is home to a thriving venture capital ecosystem that has been pouring billions of dollars into AI and ML startups and companies. The presence of major tech hubs, such as Silicon Valley, Boston, and New York, has facilitated the flow of investment capital into the AI and ML industry. For instance, in 2023, according to the S&P Global Market Intelligence data, investments in generative AI companies saw a significant increase, surpassing the decline in overall M&A activity. Private equity firms invested USD 2.18 billion in generative AI, doubling the previous year's total. This surge in capital occurred amidst a decrease in private equity-backed M&A transactions across industries in 2023. Factors such as these have made North America a predominant force in the AI and ML industry, consequently boosting the demand for AI training dataset services to support this unprecedented growth rate of the AI industry.

Some of the major players operating in the market include Google, Microsoft; Amazon Web Services, Inc.; IBM; Oracle; Alegion AI, Inc.; TELUS International; Lionbridge Technologies, LLC; Samasource Impact Sourcing, Inc.; and Appen Limited.

1.MARKET INTRODUCTION

1.1. Market Definitions
1.2. Main Objective
1.3. Stakeholders
1.4. Limitation

2.RESEARCH METHODOLOGY OR ASSUMPTION

2.1. Research Process of the AI Training Dataset Market
2.2. Research Methodology of the AI Training Dataset Market
2.3. Respondent Profile

3.EXECUTIVE SUMMARY

3.1. Industry Synopsis
3.2. Segmental Outlook
3.3. Market Growth Intensity
3.4. Regional Outlook

4.MARKET DYNAMICS

4.1. Drivers
4.2. Opportunity
4.3. Restraints
4.4. Trends
4.5. PESTEL Analysis
4.6. Demand Side Analysis
4.7. Supply Side Analysis
- 4.7.1. Merger & Acquisition
- 4.7.2. Investment Scenario
- 4.7.3. Industry Insights: Leading Startups and Their Unique Strategies

5.PRICING ANALYSIS

5.1. Regional Pricing Analysis
5.2. Price Influencing Factors

6.GLOBAL AI TRAINING DATASET MARKET REVENUE (USD BN), 2022-2032F

7.MARKET INSIGHTS BY TYPE

7.1. Text
7.2. Audio
7.3. Image
7.4. Video
7.5. Other (Sensor and Geo)

8.MARKET INSIGHTS BY DEPLOYMENT MODE

8.1. Cloud
8.2. On-Premises

9.MARKET INSIGHTS BY END-USER

9.1. IT and Telecommunication
9.2. Retail and Consumer Goods
9.3. Healthcare
9.4. Automotive
9.5. Banking, Financial Services, and Insurance (BFSI)
9.6. Others (Government and Manufacturing)

10.MARKET INSIGHTS BY REGION

10.1. North America
- 10.1.1. U.S.
- 10.1.2. Canada
- 10.1.3. Rest of North America
10.2. Europe
- 10.2.1. Germany
- 10.2.2. U.K.
- 10.2.3. France
- 10.2.4. Italy
- 10.2.5. Spain
- 10.2.6. Rest of Europe
10.3. Asia-Pacific
- 10.3.1. China
- 10.3.2. Japan
- 10.3.3. India
- 10.3.4. Australia
- 10.3.5. Rest of Asia-Pacific
10.4. Rest of World

11.VALUE CHAIN ANALYSIS

11.1. Marginal Analysis
11.2. List of Market Participants

12.COMPETITIVE LANDSCAPE

12.1. Competition Dashboard
12.2. Competitor Market Positioning Analysis
12.3. Porter Five Force Analysis

13.COMPANY PROFILED

13.1. Google
- 13.1.1. Company Overview
- 13.1.2. Key Financials
- 13.1.3. SWOT Analysis
- 13.1.4. Product Portfolio
- 13.1.5. Recent Developments
13.2. Microsoft
13.3. Amazon Web Services, Inc.
13.4. IBM
13.5. Oracle
13.6. Alegion AI, Inc
13.7. TELUS International
13.8. Lionbridge Technologies, LLC
13.9. Samasource Impact Sourcing, Inc.
13.10. Appen Limited