首頁 > 市場調查報告書 > 通訊

市場調查報告書

商品編碼

1833502

2032 年模型訓練市場合成資料產生預測：按組件、資料類型、部署模式、技術、應用、最終用戶和地區進行的全球分析

Synthetic Data Generation for Model Training Market Forecasts to 2032 - Global Analysis By Component (Tools/Platforms and Services), Data Type, Deployment Mode, Technology, Application, End User and By Geography

出版日期: 2025年10月01日 | 出版商:

Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

簡介目錄圖表

根據 Stratistics MRC 的數據，預計 2025 年全球模型訓練合成數據生成市場規模將達到 4.198 億美元，到 2032 年將達到 34.664 億美元，預測期內複合年成長率為 35.2%。

用於模型訓練的合成資料產生是指創建模擬真實世界資料特徵的人工資料集，用於訓練機器學習模型的過程。這些資料集使用諸如生成對抗網路 (GAN)、模擬和基於規則的系統等演算法生成，以確保隱私性、可擴展性和多樣性。透過提供可自訂且均衡的輸入，合成資料有助於克服資料稀缺、偏見和監管約束等限制。它可以加快實驗速度，減少對敏感或專有資料的依賴，並支援醫療保健、金融和自治系統等行業的穩健模型開發，同時遵守資料保護條例和道德標準。

對隱私保護資料的需求不斷增加

對隱私保護資料日益成長的需求是合成資料產生的關鍵驅動力。隨著企業面臨 GDPR 和 CCPA 等嚴格法規的挑戰，合成資料集提供了一個合規的真實資料替代方案。合成資料能夠在不損害使用者隱私的情況下實現安全的模型訓練，尤其是在醫療保健和金融等敏感領域。這種需求正在加速各行各業的採用，使合成資料成為在日益監管的數位環境中進行合乎道德的 AI 開發和安全資料協作的關鍵工具。

對合成數據準確性的信心限度

儘管合成數據有許多優勢，但其準確性和真實性仍面臨質疑。許多組織質疑人工生成的資料集是否能夠真正複製真實世界資料的複雜性和多變性。這種信任的缺失可能會阻礙其應用，尤其是在醫療診斷和金融建模等高風險應用中。如果沒有標準化的檢驗框架，合成數據可能會被視為不可靠，阻礙其融入關鍵任務型人工智慧工作流程，並減緩市場成長。

加速人工智慧和機器學習的採用

人工智慧和機器學習在各行各業的快速發展為合成數據生成帶來了巨大的機會。隨著企業尋求擴充性且多樣化的資料集來訓練其模型，合成資料提供了一種經濟高效且靈活的解決方案。它可以加快實驗速度，減少對專有數據的依賴，並支援自主系統、預測分析和自然語言處理等領域的創新。人工智慧應用的激增正在推動對合成數據的需求，並將其定位為現代模型開發的基石。

計算成本高

產生高品質的合成數據需要大量的計算資源，這阻礙了其廣泛應用。像 GAN 和模擬這樣的先進技術需要強大的硬體和專業知識，這對於中小企業來說成本高昂。高昂的基礎設施和營運成本可能會限制其應用，尤其是在新興市場和資源受限的行業。如果沒有經濟實惠的解決方案，許多組織可能無法享受合成數據的優勢，從而減緩市場滲透和創新。

COVID-19的影響：

新冠疫情加速了數位轉型，凸顯了對安全、可擴展數據解決方案的需求。由於現實世界資料存取受限以及隱私問題日益加劇，合成資料已成為模型訓練的寶貴工具，在疫情封鎖期間，協助醫療、物流和遠端服務領域的人工智慧持續發展。疫情凸顯了靈活且符合隱私要求的資料產生的重要性，並刺激了對合成資料技術的長期投資，以支援具有彈性且面向未來的人工智慧基礎設施。

語音辨識預計將成為預測期內最大的細分市場

語音辨識領域預計將在預測期內佔據最大的市場佔有率，因為它依賴大量多樣化的資料集來訓練語音模型。合成資料能夠創造多語言、口音豐富且噪音變化的語音輸入，從而提高模型的準確性和整體性。隨著語音介面成為設備和服務的主流，對可擴展、符合隱私要求的訓練資料的需求也日益成長。合成資料支援虛擬助理、轉錄工具和無障礙技術的創新，從而確保其在市場上的主導地位。

預計醫療診斷領域在預測期內將實現最高複合年成長率

由於對安全且多樣化的醫療資料集的需求，預計醫療診斷領域將在預測期內實現最高成長率。合成資料能夠在不洩漏病患資訊的情況下進行模型訓練，從而確保符合隱私法規。合成數據支持疾病預測、影像分析和個人化治療計劃等應用。隨著人工智慧在醫療保健領域的應用加速，合成數據提供了一種可擴展的解決方案，可以克服數據稀缺和偏見，從而推動診斷領域的快速發展並改變臨床決策。

比最大的地區

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其先進的人工智慧生態系統、強大的監管框架以及合成數據技術的早期應用。該地區領先的科技公司和研究機構正在大力投資隱私保護資料解決方案。強大的基礎設施、熟練的人才和有利於創新的政策支持其在醫療保健、金融和自治系統等領域的廣泛應用，鞏固了北美在合成數據生成領域的領先地位。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這得益於數位化的快速發展、人工智慧舉措的不斷擴展以及資料隱私意識的不斷增強。印度、中國和東南亞等新興經濟體正在投資合成數據，以克服數據存取挑戰並支援可擴展的模型訓練。政府支持的創新項目以及醫療保健、教育和智慧城市領域對人工智慧日益成長的需求正在推動其應用。該地區的蓬勃發展和技術驅動型思維模式使其成為合成數據的高速市場。

免費客製化服務：

此報告的訂閱者可以使用以下免費自訂選項之一：

公司簡介
- 對最多三家其他市場公司進行全面分析
- 主要企業的SWOT分析（最多3家公司）
區域細分
- 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
競爭基準化分析
- 根據產品系列、地理分佈和策略聯盟對主要企業基準化分析

北美洲
- 美國
- 加拿大
- 墨西哥
歐洲
- 德國
- 英國
- 義大利
- 法國
- 西班牙
- 其他歐洲國家
亞太地區
- 日本
- 中國
- 印度
- 澳洲
- 紐西蘭
- 韓國
- 其他亞太地區
南美洲
- 阿根廷
- 巴西
- 智利
- 其他南美
中東和非洲
- 沙烏地阿拉伯
- 阿拉伯聯合大公國
- 卡達
- 南非
- 其他中東和非洲地區

第12章重大進展

協議、夥伴關係、合作和合資企業
收購與合併
新產品發布
業務擴展
其他關鍵策略

第13章：企業概況

NVIDIA Corporation
Synthera AI
IBM Corporation
brewdata
Microsoft Corporation
Lemon AI
Google LLC
Sightwise
Amazon Web Services（AWS）
Simulacra Synthetic Data Studio
Synthetic Data, Inc.
Gretel.ai
Hazy
TruEra
Synthesis AI

簡介目錄圖表

Product Code: SMRC31335

According to Stratistics MRC, the Global Synthetic Data Generation for Model Training Market is accounted for $419.8 million in 2025 and is expected to reach $3,466.4 million by 2032 growing at a CAGR of 35.2% during the forecast period. Synthetic Data Generation for Model Training refers to the process of creating artificial datasets that mimic real-world data characteristics for use in training machine learning models. These datasets are generated using algorithms such as generative adversarial networks (GANs), simulations, or rule-based systems, ensuring privacy, scalability, and diversity. Synthetic data helps overcome limitations like data scarcity, bias, and regulatory constraints by providing customizable, balanced inputs. It enables faster experimentation, reduces dependency on sensitive or proprietary data, and supports robust model development across industries including healthcare, finance, and autonomous systems, while maintaining compliance with data protection regulations and ethical standards.

Market Dynamics:

Driver:

Growing demand for privacy-preserving data

The rising need for privacy-preserving data is a major driver of synthetic data generation. As organizations face stricter regulations like GDPR and CCPA, synthetic datasets offer a compliant alternative to real data. They enable secure model training without compromising user privacy, especially in sensitive sectors like healthcare and finance. This demand is accelerating adoption across industries, making synthetic data a critical tool for ethical AI development and secure data collaboration in increasingly regulated digital environments.

Restraint:

Limited trust in synthetic data accuracy

Despite its advantages, synthetic data faces skepticism regarding its accuracy and realism. Many organizations question whether artificially generated datasets can truly replicate the complexity and variability of real-world data. This lack of trust can hinder adoption, especially in high-stakes applications like medical diagnostics or financial modeling. Without standardized validation frameworks, synthetic data may be perceived as unreliable, creating barriers to its integration into mission-critical AI workflows and slowing market growth.

Opportunity:

Acceleration of AI and ML adoption

The rapid expansion of AI and machine learning across industries presents a major opportunity for synthetic data generation. As organizations seek scalable, diverse datasets to train models, synthetic data offers a cost-effective and flexible solution. It enables faster experimentation, reduces dependency on proprietary data, and supports innovation in areas like autonomous systems, predictive analytics, and natural language processing. This surge in AI adoption fuels demand for synthetic data, positioning it as a foundational element of modern model development.

Threat:

High computational costs

Generating high-quality synthetic data requires significant computational resources, posing a threat to widespread adoption. Advanced techniques like GANs and simulations demand powerful hardware and specialized expertise, which can be costly for smaller enterprises. These high infrastructure and operational expenses may limit accessibility, especially in emerging markets or resource-constrained sectors. Without affordable solutions, the benefits of synthetic data may remain out of reach for many organizations, slowing market penetration and innovation.

Covid-19 Impact:

The COVID-19 pandemic accelerated digital transformation and highlighted the need for secure, scalable data solutions. With limited access to real-world data and increased privacy concerns, synthetic data emerged as a valuable tool for model training. It enabled continued AI development in healthcare, logistics, and remote services during lockdowns. The pandemic underscored the importance of flexible, privacy-compliant data generation, driving long-term investment in synthetic data technologies to support resilient, future-ready AI infrastructures.

The speech recognition segment is expected to be the largest during the forecast period

The speech recognition segment is expected to account for the largest market share during the forecast period due to its reliance on large, diverse datasets for training voice models. Synthetic data enables the creation of multilingual, accent-rich, and noise-varied speech inputs, enhancing model accuracy and inclusivity. As voice interfaces become mainstream across devices and services, demand for scalable, privacy-compliant training data grows. Synthetic data supports innovation in virtual assistants, transcription tools, and accessibility technologies, securing its leading position in the market.

The healthcare diagnostics segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare diagnostics segment is predicted to witness the highest growth rate owing to the need for secure, diverse medical datasets. Synthetic data enables model training without exposing patient information, ensuring compliance with privacy regulations. It supports applications like disease prediction, imaging analysis, and personalized treatment planning. As AI adoption in healthcare accelerates, synthetic data offers a scalable solution to overcome data scarcity and bias, fueling rapid growth in diagnostics and transforming clinical decision-making.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share because of its advanced AI ecosystem, strong regulatory frameworks, and early adoption of synthetic data technologies. Leading tech companies and research institutions in the region are investing heavily in privacy-preserving data solutions. The presence of robust infrastructure, skilled talent, and innovation-friendly policies supports widespread deployment across sectors like healthcare, finance, and autonomous systems, solidifying North America's leadership in synthetic data generation.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to rapid digitalization, expanding AI initiatives, and growing awareness of data privacy. Emerging economies like India, China, and Southeast Asia are investing in synthetic data to overcome data access challenges and support scalable model training. Government-backed innovation programs and increasing demand for AI in healthcare, education, and smart cities drive adoption. The region's dynamic growth and tech-forward mindset position it as a high-velocity market for synthetic data.

Key players in the market

Some of the key players in Synthetic Data Generation for Model Training Market include NVIDIA Corporation, Synthera AI, IBM Corporation, brewdata, Microsoft Corporation, Lemon AI, Google LLC, Sightwise, Amazon Web Services (AWS), Simulacra Synthetic Data Studio, Synthetic Data, Inc., Gretel.ai, Hazy, TruEra and Synthesis AI.

Key Developments:

In September 2025, Keepler and AWS have entered a strategic collaboration to accelerate the adoption of Generative AI in Europe. Keepler, as an AWS Premier Tier Partner, will harness its AI/data expertise with AWS infrastructure to build autonomous AI agents and bespoke enterprise solutions-spanning supply chain, customer experience, and more.

In April 2025, EPAM is deepening its strategic collaboration with AWS to push generative AI across enterprise modernization efforts. The expanded agreement enables EPAM to integrate AWS GenAI services like Amazon Bedrock into its AI/Run(TM) platform to help clients build specialized AI agents, automate workflows, migrate workloads, and scale applications efficiently and securely.

Components Covered:

Tools/Platforms
Services

Data Types Covered:

Tabular Data
Time-Series Data
Image & Video Data
Audio Data
Text Data
Other Data Types

Deployment Modes Covered:

On-Premises
Cloud-Based

Technologies Covered:

Machine Learning
Predictive Analytics
Deep Learning
Speech Recognition
Natural Language Processing (NLP)
Computer Vision

Applications Covered:

Data Privacy & Security
Autonomous Systems
Data Augmentation
Robotics
Simulation & Testing
Healthcare Diagnostics
Algorithm Validation
Fraud Detection
Other Applications

End Users Covered:

Media & Entertainment
Manufacturing
Government & Defense
Retail & E-commerce
IT & Telecommunications
Automotive & Transportation
Energy & Utilities
Other End Users

Regions Covered:

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- Italy
- France
- Spain
- Rest of Europe
Asia Pacific
- Japan
- China
- India
- Australia
- New Zealand
- South Korea
- Rest of Asia Pacific
South America
- Argentina
- Brazil
- Chile
- Rest of South America
Middle East & Africa
- Saudi Arabia
- UAE
- Qatar
- South Africa
- Rest of Middle East & Africa

What our report offers:

Market share assessments for the regional and country-level segments
Strategic recommendations for the new entrants
Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
Strategic recommendations in key business segments based on the market estimations
Competitive landscaping mapping the key common trends
Company profiling with detailed strategies, financials, and recent developments
Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

Company Profiling
- Comprehensive profiling of additional market players (up to 3)
- SWOT Analysis of key players (up to 3)
Regional Segmentation
- Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
Competitive Benchmarking
- Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

1 Executive Summary

2 Preface

2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
- 2.4.1 Data Mining
- 2.4.2 Data Analysis
- 2.4.3 Data Validation
- 2.4.4 Research Approach
2.5 Research Sources
- 2.5.1 Primary Research Sources
- 2.5.2 Secondary Research Sources
- 2.5.3 Assumptions

3 Market Trend Analysis

3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19

4 Porters Five Force Analysis

4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry

5 Global Synthetic Data Generation for Model Training Market, By Component

5.1 Introduction
5.2 Tools/Platforms
5.3 Services
- 5.3.1 Consulting
- 5.3.2 Training & Support
- 5.3.3 Managed Services

6 Global Synthetic Data Generation for Model Training Market, By Data Type

6.1 Introduction
6.2 Tabular Data
6.3 Time-Series Data
6.4 Image & Video Data
6.5 Audio Data
6.6 Text Data
6.7 Other Data Types

7 Global Synthetic Data Generation for Model Training Market, By Deployment Mode

7.1 Introduction
7.2 On-Premises
7.3 Cloud-Based

8 Global Synthetic Data Generation for Model Training Market, By Technology

8.1 Introduction
8.2 Machine Learning
8.3 Predictive Analytics
8.4 Deep Learning
8.5 Speech Recognition
8.6 Natural Language Processing (NLP)
8.7 Computer Vision

9 Global Synthetic Data Generation for Model Training Market, By Application

9.1 Introduction
9.2 Data Privacy & Security
9.3 Autonomous Systems
9.4 Data Augmentation
9.5 Robotics
9.6 Simulation & Testing
9.7 Healthcare Diagnostics
9.8 Algorithm Validation
9.9 Fraud Detection
9.10 Other Applications

10 Global Synthetic Data Generation for Model Training Market, By End User

10.1 Healthcare & Life Sciences
10.2 Media & Entertainment
10.3 Manufacturing
10.4 Government & Defense
10.5 Retail & E-commerce
10.6 IT & Telecommunications
10.7 Automotive & Transportation
10.8 Energy & Utilities
10.9 Other End Users

11 Global Synthetic Data Generation for Model Training Market, By Geography

11.1 Introduction
11.2 North America
- 11.2.1 US
- 11.2.2 Canada
- 11.2.3 Mexico
11.3 Europe
- 11.3.1 Germany
- 11.3.2 UK
- 11.3.3 Italy
- 11.3.4 France
- 11.3.5 Spain
- 11.3.6 Rest of Europe
11.4 Asia Pacific
- 11.4.1 Japan
- 11.4.2 China
- 11.4.3 India
- 11.4.4 Australia
- 11.4.5 New Zealand
- 11.4.6 South Korea
- 11.4.7 Rest of Asia Pacific
11.5 South America
- 11.5.1 Argentina
- 11.5.2 Brazil
- 11.5.3 Chile
- 11.5.4 Rest of South America
11.6 Middle East & Africa
- 11.6.1 Saudi Arabia
- 11.6.2 UAE
- 11.6.3 Qatar
- 11.6.4 South Africa
- 11.6.5 Rest of Middle East & Africa

12 Key Developments

12.1 Agreements, Partnerships, Collaborations and Joint Ventures
12.2 Acquisitions & Mergers
12.3 New Product Launch
12.4 Expansions
12.5 Other Key Strategies

13 Company Profiling

13.1 NVIDIA Corporation
13.2 Synthera AI
13.3 IBM Corporation
13.4 brewdata
13.5 Microsoft Corporation
13.6 Lemon AI
13.7 Google LLC
13.8 Sightwise
13.9 Amazon Web Services (AWS)
13.10 Simulacra Synthetic Data Studio
13.11 Synthetic Data, Inc.
13.12 Gretel.ai
13.13 Hazy
13.14 TruEra
13.15 Synthesis AI

簡介目錄圖表

List of Tables

Table 1 Global Synthetic Data Generation for Model Training Market Outlook, By Region (2024-2032) ($MN)
Table 2 Global Synthetic Data Generation for Model Training Market Outlook, By Component (2024-2032) ($MN)
Table 3 Global Synthetic Data Generation for Model Training Market Outlook, By Tools/Platforms (2024-2032) ($MN)
Table 4 Global Synthetic Data Generation for Model Training Market Outlook, By Services (2024-2032) ($MN)
Table 5 Global Synthetic Data Generation for Model Training Market Outlook, By Consulting (2024-2032) ($MN)
Table 6 Global Synthetic Data Generation for Model Training Market Outlook, By Training & Support (2024-2032) ($MN)
Table 7 Global Synthetic Data Generation for Model Training Market Outlook, By Managed Services (2024-2032) ($MN)
Table 8 Global Synthetic Data Generation for Model Training Market Outlook, By Data Type (2024-2032) ($MN)
Table 9 Global Synthetic Data Generation for Model Training Market Outlook, By Tabular Data (2024-2032) ($MN)
Table 10 Global Synthetic Data Generation for Model Training Market Outlook, By Time-Series Data (2024-2032) ($MN)
Table 11 Global Synthetic Data Generation for Model Training Market Outlook, By Image & Video Data (2024-2032) ($MN)
Table 12 Global Synthetic Data Generation for Model Training Market Outlook, By Audio Data (2024-2032) ($MN)
Table 13 Global Synthetic Data Generation for Model Training Market Outlook, By Text Data (2024-2032) ($MN)
Table 14 Global Synthetic Data Generation for Model Training Market Outlook, By Other Data Types (2024-2032) ($MN)
Table 15 Global Synthetic Data Generation for Model Training Market Outlook, By Deployment Mode (2024-2032) ($MN)
Table 16 Global Synthetic Data Generation for Model Training Market Outlook, By On-Premises (2024-2032) ($MN)
Table 17 Global Synthetic Data Generation for Model Training Market Outlook, By Cloud-Based (2024-2032) ($MN)
Table 18 Global Synthetic Data Generation for Model Training Market Outlook, By Technology (2024-2032) ($MN)
Table 19 Global Synthetic Data Generation for Model Training Market Outlook, By Machine Learning (2024-2032) ($MN)
Table 20 Global Synthetic Data Generation for Model Training Market Outlook, By Predictive Analytics (2024-2032) ($MN)
Table 21 Global Synthetic Data Generation for Model Training Market Outlook, By Deep Learning (2024-2032) ($MN)
Table 22 Global Synthetic Data Generation for Model Training Market Outlook, By Speech Recognition (2024-2032) ($MN)
Table 23 Global Synthetic Data Generation for Model Training Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
Table 24 Global Synthetic Data Generation for Model Training Market Outlook, By Computer Vision (2024-2032) ($MN)
Table 25 Global Synthetic Data Generation for Model Training Market Outlook, By Application (2024-2032) ($MN)
Table 26 Global Synthetic Data Generation for Model Training Market Outlook, By Data Privacy & Security (2024-2032) ($MN)
Table 27 Global Synthetic Data Generation for Model Training Market Outlook, By Autonomous Systems (2024-2032) ($MN)
Table 28 Global Synthetic Data Generation for Model Training Market Outlook, By Data Augmentation (2024-2032) ($MN)
Table 29 Global Synthetic Data Generation for Model Training Market Outlook, By Robotics (2024-2032) ($MN)
Table 30 Global Synthetic Data Generation for Model Training Market Outlook, By Simulation & Testing (2024-2032) ($MN)
Table 31 Global Synthetic Data Generation for Model Training Market Outlook, By Healthcare Diagnostics (2024-2032) ($MN)
Table 32 Global Synthetic Data Generation for Model Training Market Outlook, By Algorithm Validation (2024-2032) ($MN)
Table 33 Global Synthetic Data Generation for Model Training Market Outlook, By Fraud Detection (2024-2032) ($MN)
Table 34 Global Synthetic Data Generation for Model Training Market Outlook, By Other Applications (2024-2032) ($MN)
Table 35 Global Synthetic Data Generation for Model Training Market Outlook, By End User (2024-2032) ($MN)
Table 36 Global Synthetic Data Generation for Model Training Market Outlook, By Media & Entertainment (2024-2032) ($MN)
Table 37 Global Synthetic Data Generation for Model Training Market Outlook, By Manufacturing (2024-2032) ($MN)
Table 38 Global Synthetic Data Generation for Model Training Market Outlook, By Government & Defense (2024-2032) ($MN)
Table 39 Global Synthetic Data Generation for Model Training Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
Table 40 Global Synthetic Data Generation for Model Training Market Outlook, By IT & Telecommunications (2024-2032) ($MN)
Table 41 Global Synthetic Data Generation for Model Training Market Outlook, By Automotive & Transportation (2024-2032) ($MN)
Table 42 Global Synthetic Data Generation for Model Training Market Outlook, By Energy & Utilities (2024-2032) ($MN)
Table 43 Global Synthetic Data Generation for Model Training Market Outlook, By Other End Users (2024-2032) ($MN)