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市场调查报告书
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全球机器学习遗忘解决方案市场：预测至 2032 年—按解决方案类型、遗忘方法、部署方式、组织规模、应用程式、最终用户和地区进行分析

Machine Unlearning Solutions Market Forecasts to 2032 - Global Analysis By Solution Type, Unlearning Technique, Deployment Mode, Organization Size, Application, End User, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，全球机器学习反学习解决方案市场预计到 2025 年将达到 1.5 亿美元，到 2032 年将达到 27.3 亿美元，预测期内复合年增长率为 51.2%。

机器学习遗忘解决方案旨在无需重新训练即可从已训练的机器学习模型中移除特定资料点。这些解决方案对于隐私法规、偏差缓解和纠正错误资料至关重要，它们使模型能够「遗忘」。随着资料隐私法律的日益完善和人工智慧伦理的日益受到重视，这项技术对于维护合规、准确、公平且可高效更新和纠正的人工智慧系统至关重要。

日益严格的数据隐私法规要求删除数据

随着 GDPR 和 CCPA 等全球资料隐私法律以及各国新法规的兴起，企业必须应要求删除个人资料。这推动了对机器学习「遗忘」解决方案的需求，该方案旨在确保人工智慧模型合规，而无需从头开始重新训练。此外，金融、医疗保健和社群媒体等处理敏感资讯的行业正在采用自动化「遗忘」流程，以降低法律风险、维护消费者信任并支持符合伦理的人工智慧倡议。合规要求持续推动全球此类解决方案的普及。

性能对模型精度和效率的影响

机器学习的遗忘操作可能会降低模型效能，影响准确性和计算效率。从已训练的模型中移除资料点可能会引入资料不一致或需要部分重新训练，从而增加处理时间和资源消费量。此外，复杂的遗忘演算法可能会对IT基础设施造成压力，限制小规模组织的采用。平衡合规性和营运效率仍然是一项关键挑战，因为组织必须在保持模型可靠性的同时，有效清除敏感数据，并且不能中断现有的工作流程。

与人工智慧管治和MLOps平台集成

将机器学习遗忘解决方案与人工智慧管治和机器学习运作 (MLOps) 框架相集成，可简化合规性、监控和模型生命週期管理。此类整合可实现资料删除请求自动化、审核追踪和版本控制，从而减少人工监管。此外，组织还可以将遗忘与模型可解释性和公平性工具结合，提高透明度和信任度。这些协同效应为提供整合解决方案的供应商创造了市场机会，这些解决方案能够简化监管合规性并支援各行业的稳健人工智慧营运。

资料删除不彻底可能会造成合规风险。

部分或无效的去训练会导致残留数据，使组织面临法律处罚、监管审查和声誉损害。不完整的去训练会削弱信任，降低人工智慧模型的可靠性，尤其是在涉及敏感个人或财务资讯的领域。此外，复杂的模型架构使得完全去训练变得困难，需要持续的监控和检验。

新冠疫情的影响：

新冠疫情加速了各产业的数位转型，并推动了人工智慧（AI）的广泛应用，同时也加剧了人们对资料隐私的担忧。远距办公、云端迁移和线上服务产生了大量的个人数据，凸显了机器学习「遗忘」解决方案的必要性。为了在快速部署过程中保护敏感讯息，各组织优先考虑合规自动化和安全的AI模型管理。这促使企业加大对AI管治框架和整合「遗忘」工具的投资，以确保合规性并增强对数位服务的信任。

预计在预测期内，遗忘学习的群体规模将最大。

预计在预测期内，近似遗忘技术将占据最大的市场份额。企业之所以青睐近似遗忘技术，是因为它既能降低重新训练的成本和时间，又能符合隐私法规。该技术适用于各种人工智慧架构，因此无论大中小型企业都能采用。此外，供应商正不断优化这些技术，以提高准确性、审核以及与现有机器学习运维流程的整合度，从而巩固其市场领先地位。高效性、扩充性和合规性这三者的完美结合，正推动该技术在机器学习遗忘解决方案领域占据主导地位。

预计在预测期内，云端基础的细分市场将以最高的复合年增长率成长。

预计在预测期内，云端基础方案将实现最高成长率。云端基础机器学习解决方案具有灵活性、扩充性和更低的初始成本，以便于各种规模的组织快速部署。它们提供集中管理、自动更新以及与云端人工智慧服务的集成，从而提高营运效率。此外，云端传输支援全球访问，并允许在数据处理或学习需求激增时实现无缝扩展。组织可以受益于基础设施负担的减轻和基于订阅的定价模式，这使得云端基础解决方案成为市场中成长最快的细分领域。

占比最高的地区：

在预测期内，北美预计将占据最大的市场份额，这主要得益于其严格的隐私法规、早期人工智慧应用以及众多主要技术供应商的存在。医疗保健、金融和科技业的公司正越来越多地采用机器学习反学习解决方案来满足合规性要求。此外，强大的IT基础设施、云端技术的广泛应用以及高额的研发投入也为先进反学习技术的快速部署和整合提供了支援。这些因素共同促成了北美成为机器学习反学习解决方案最大的区域市场。

复合年增长率最高的地区：

在预测期内，由于包括GDPR在内的严格资料保护条例以及社会对隐私权日益增强的意识，欧洲预计将呈现最高的复合年增长率。各组织正在采用机器学习「遗忘」技术，以在遵守严格法律义务的同时保持人工智慧的效能。此外，该地区对人工智慧研究、云端基础设施和专注于隐私的新兴企业的投资正在推动创新和应用。政府、企业和供应商之间的合作倡议正在加速可扩展「遗忘」解决方案的部署，使欧洲成为预测期内成长最快的区域市场。

免费客製化服务

订阅本报告的用户可从以下免费自订选项中选择一项：

  • 公司简介
    • 对最多三家其他公司进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域分类
    • 根据客户兴趣对主要国家进行市场估算、预测和复合年增长率分析（註：基于可行性检查）
  • 竞争基准化分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 引言

  • 概述
  • 相关利益者
  • 分析范围
  • 分析方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 分析方法
  • 分析材料
    • 原始研究资料
    • 二手研究资讯来源
    • 先决条件

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 市场机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代产品的威胁
  • 新参与企业的威胁
  • 公司间的竞争

5. 全球机器学习反学习解决方案市场（按解决方案类型划分）

  • 软体/工具、平台
    • 独立式遗忘学习软体
    • 整合式MLOps/模型管治平台
  • 服务
    • 专业服务
      • 咨询和实施
      • 支援和维护
    • 託管服务/遗忘即服务 (UaaS)

6. 全球机器学习反学习解决方案市场（以反学习方法论划分）

  • 精准的遗忘
  • 近似于遗忘
    • 影响函数型方法
    • 基于最佳化的方法
    • 其他近似演算法

7. 全球机器学习反学习解决方案市场（以部署方式划分）

  • 云端基础的
  • 本地部署

8. 全球机器学习反学习解决方案市场（按组织规模划分）

  • 大公司
  • 小型企业

9. 全球机器学习反学习解决方案市场（按类型划分）

  • 减少偏见和促进公平
  • 安全与攻击缓解
  • 遵守资料隐私法规
  • 模型生命週期管理和效能最佳化
  • 其他用途

第十章：全球机器学习反学习解决方案市场（以最终用户划分）

  • 银行、金融服务和保险 (BFSI)
  • 医学与生命科​​学
  • 资讯科技/通讯
  • 零售与电子商务
  • 政府/公共部门
  • 汽车/製造业
  • 媒体与娱乐
  • 其他最终用户

第十一章 全球机器学习反学习解决方案市场（按地区划分）

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 亚太其他地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美洲
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章：主要趋势

  • 合约、商业伙伴关係和合资企业
  • 企业合併（M&A）
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十三章：公司简介

  • Amazon Web Services, Inc.
  • Baidu, Inc.
  • Google LLC
  • H2O.ai, Inc.
  • Hewlett-Packard Enterprise Development LP
  • Intel Corporation
  • IBM Corporation
  • Microsoft Corporation
  • SAS Institute Inc.
  • SAP SE
  • DataRobot, Inc.
  • C3.ai, Inc.
  • OpenAI, Inc.
  • Graphcore Ltd.
  • SUALAB Inc.
  • Megvii Technology Limited
  • Elliptic Labs Inc.
  • Handshakes Inc.
  • IntelliVIX Inc.
  • Twigfarm Inc.
Product Code: SMRC31918

According to Stratistics MRC, the Global Machine Unlearning Solutions Market is accounted for $0.15 billion in 2025 and is expected to reach $2.73 billion by 2032 growing at a CAGR of 51.2% during the forecast period. Machine unlearning solutions address the need to remove specific data points from trained machine learning models without full retraining. Crucial for privacy regulations, bias mitigation, and correcting erroneous data, these solutions allow models to "forget." As data privacy laws tighten and AI ethics gain prominence, this technology is vital for maintaining compliant, accurate, and fair AI systems, ensuring they can be efficiently updated and corrected.

Market Dynamics:

Driver:

Increasing data privacy regulations requiring data deletion

The rise of global data privacy laws such as GDPR, CCPA, and emerging national regulations compels organizations to delete personal data upon request. This drives demand for machine unlearning solutions that ensure AI models comply without retraining from scratch. Furthermore, industries handling sensitive information, including finance, healthcares, and social media, are adopting automated unlearning processes to mitigate legal risks, maintain consumer trust, and support ethical AI initiatives. Compliance obligations continue to expand adoption worldwide.

Restraint:

Performance impact on model accuracy and efficiency

Implementing machine unlearning can degrade model performance, affecting accuracy and computational efficiency. Removing data points from trained models may introduce inconsistencies or require partial retraining, which increases processing time and resource consumption. Additionally, complex unlearning algorithms may strain IT infrastructure, deterring smaller organizations from adoption. Balancing regulatory compliance with operational efficiency remains a significant challenge, as organizations must maintain model reliability while ensuring sensitive data is effectively purged without disrupting existing workflows.

Opportunity:

Integration with AI governance and MLOps platforms

Machine unlearning solutions can be integrated with AI governance and MLOps frameworks to streamline compliance, monitoring, and model lifecycle management. Such integration enables automated data deletion requests, audit trails, and version control, reducing manual oversight. Moreover, organizations can combine unlearning with model interpretability and fairness tools, enhancing transparency and trust. This synergy creates market opportunities for vendors offering unified solutions that simplify regulatory adherence while supporting robust AI operations across industries.

Threat:

Potential for incomplete data removal creating compliance risks

Partial or ineffective unlearning may leave residual data, exposing organizations to legal penalties, regulatory scrutiny, and reputational damage. Incomplete removal can compromise trust and reduce the reliability of AI models, especially in sectors handling sensitive personal or financial information. Additionally, complex model architectures make thorough deletion challenging, requiring ongoing monitoring and validation.

Covid-19 Impact:

The Covid-19 pandemic accelerated digital transformation, increasing AI adoption across sectors while simultaneously amplifying concerns about data privacy. Remote work, cloud migration, and online services generated higher volumes of personal data, highlighting the need for machine unlearning solutions. Organizations prioritized compliance automation and secure AI model management to protect sensitive information amid rapid deployment. This led to accelerated investments in unlearning tools integrated with AI governance frameworks, ensuring regulatory adherence and reinforcing trust in digital services.

The approximate unlearning segment is expected to be the largest during the forecast period

The approximate unlearning segment is expected to account for the largest market share during the forecast period. Organizations favor approximate unlearning because it reduces retraining costs and time while achieving compliance with privacy laws. Its applicability across diverse AI architectures enables adoption by both large enterprises and SMEs. Moreover, vendors increasingly optimize these methods for accuracy retention, auditability, and integration with existing MLOps pipelines, reinforcing their market leadership. The combination of efficiency, scalability, and regulatory alignment ensures the segment dominates the machine unlearning solutions landscape.

The cloud-based segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the cloud-based segment is predicted to witness the highest growth rate. Cloud-based machine unlearning solutions offer flexibility, scalability, and lower upfront costs, facilitating rapid deployment for organizations of all sizes. They provide centralized management, automated updates, and integration with cloud AI services, enhancing operational efficiency. Additionally, cloud delivery supports global accessibility and seamless scaling during spikes in data processing or unlearning requests. Organizations benefit from reduced infrastructure burden and subscription-based pricing, making cloud-based solutions the fastest-growing segment in the market.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market sharedue to stringent privacy regulations, early AI adoption, and the presence of major technology vendors. Enterprises across healthcare, finance, and technology sectors are increasingly implementing machine unlearning solutions to meet compliance requirements. Furthermore, strong IT infrastructure, cloud adoption, and high R&D investment support rapid deployment and integration of advanced unlearning techniques. These factors collectively position North America as the largest regional market for machine unlearning solutions.

Region with highest CAGR:

Over the forecast period, the Europe region is anticipated to exhibit the highest CAGR driven by strict data protection regulations, including GDPR, and growing public awareness of privacy rights. Organizations are adopting machine unlearning to comply with rigorous legal mandates while preserving AI performance. Moreover, the region's investment in AI research, cloud infrastructure, and privacy-centric startups fosters innovation and adoption. Collaborative initiatives between governments, enterprises, and vendors accelerate deployment of scalable unlearning solutions, making Europe the fastest-growing regional market in the forecast period.

Key players in the market

Some of the key players in Machine Unlearning Solutions Market include Amazon Web Services, Inc., Baidu, Inc., Google LLC, H2O.ai, Inc., Hewlett-Packard Enterprise Development LP, Intel Corporation, IBM Corporation, Microsoft Corporation, SAS Institute Inc., SAP SE, DataRobot, Inc., C3.ai, Inc., OpenAI, Inc., Graphcore Ltd., SUALAB Inc., Megvii Technology Limited, Elliptic Labs Inc., Handshakes Inc., IntelliVIX Inc., and Twigfarm Inc.

Key Developments:

In October 2025, Google for Startups announced its Gemini Founders Forum, including Hirundo, a startup powered by Google Cloud's AI stack focused on machine unlearning. This indicates Google's ongoing support for unlearning R&D across its DeepMind and Gemini ecosystems.

In October 2025, Microsoft's Azure forum outlined best practices for approximate unlearning, recommending parameter-efficient fine-tuning and edit tracking. Microsoft research groups continue publishing policy and technical analyses under projects like "Lifelong Model Editing" and "Physics of AGI".

In October 2024, IBM published research on "Split, Unlearn, Merge" (SPUNGE), a framework designed to amplify the effectiveness of unlearning methods in LLMs. SPUNGE leverages data attributes to enhance unlearning performance, aiming to improve model safety by removing harmful behaviors and knowledge.

Solution Types Covered:

  • Software/Tools & Platforms
  • Services

Unlearning Techniques Covered:

  • Exact Unlearning
  • Approximate Unlearning

Deployment Modes Covered:

  • Cloud-based
  • On-premises

Organization Sizes Covered:

  • Large Enterprises
  • Small and Medium-sized Enterprises (SMEs)

Applications Covered:

  • Bias and Fairness Mitigation
  • Security and Attack Mitigation
  • Compliance with Data Privacy Regulations
  • Model Lifecycle Management and Performance Optimization
  • Other Applications

End Users Covered:

  • BFSI (Banking, Financial Services, and Insurance)
  • Healthcare & Life Sciences
  • IT & Telecommunications
  • Retail & E-commerce
  • Government & Public Sector
  • Automotive & Manufacturing
  • Media & Entertainment
  • 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

Table of Contents

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 Application Analysis
  • 3.7 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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 Machine Unlearning Solutions Market, By Solution Type

  • 5.1 Introduction
  • 5.2 Software/Tools & Platforms
    • 5.2.1 Standalone Unlearning Software
    • 5.2.2 Integrated MLOps/Model Governance Platforms
  • 5.3 Services
    • 5.3.1 Professional Services
      • 5.3.1.1 Consulting & Implementation
      • 5.3.1.2 Support & Maintenance
    • 5.3.2 Managed Services / Unlearning-as-a-Service (UaaS)

6 Global Machine Unlearning Solutions Market, By Unlearning Technique

  • 6.1 Introduction
  • 6.2 Exact Unlearning
  • 6.3 Approximate Unlearning
    • 6.3.1 Influence Function-based Methods
    • 6.3.2 Optimization-based Methods
    • 6.3.3 Other Approximation Algorithms

7 Global Machine Unlearning Solutions Market, By Deployment Mode

  • 7.1 Introduction
  • 7.2 Cloud-based
  • 7.3 On-premises

8 Global Machine Unlearning Solutions Market, By Organization Size

  • 8.1 Introduction
  • 8.2 Large Enterprises
  • 8.3 Small and Medium-sized Enterprises (SMEs)

9 Global Machine Unlearning Solutions Market, By Application

  • 9.1 Introduction
  • 9.2 Bias and Fairness Mitigation
  • 9.3 Security and Attack Mitigation
  • 9.4 Compliance with Data Privacy Regulations
  • 9.5 Model Lifecycle Management and Performance Optimization
  • 9.6 Other Applications

10 Global Machine Unlearning Solutions Market, By End User

  • 10.1 Introduction
  • 10.2 BFSI (Banking, Financial Services, and Insurance)
  • 10.3 Healthcare & Life Sciences
  • 10.4 IT & Telecommunications
  • 10.5 Retail & E-commerce
  • 10.6 Government & Public Sector
  • 10.7 Automotive & Manufacturing
  • 10.8 Media & Entertainment
  • 10.9 Other End Users

11 Global Machine Unlearning Solutions 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 Amazon Web Services, Inc.
  • 13.2 Baidu, Inc.
  • 13.3 Google LLC
  • 13.4 H2O.ai, Inc.
  • 13.5 Hewlett-Packard Enterprise Development LP
  • 13.6 Intel Corporation
  • 13.7 IBM Corporation
  • 13.8 Microsoft Corporation
  • 13.9 SAS Institute Inc.
  • 13.10 SAP SE
  • 13.11 DataRobot, Inc.
  • 13.12 C3.ai, Inc.
  • 13.13 OpenAI, Inc.
  • 13.14 Graphcore Ltd.
  • 13.15 SUALAB Inc.
  • 13.16 Megvii Technology Limited
  • 13.17 Elliptic Labs Inc.
  • 13.18 Handshakes Inc.
  • 13.19 IntelliVIX Inc.
  • 13.20 Twigfarm Inc.

List of Tables

  • Table 1 Global Machine Unlearning Solutions Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Machine Unlearning Solutions Market Outlook, By Solution Type (2024-2032) ($MN)
  • Table 3 Global Machine Unlearning Solutions Market Outlook, By Software/Tools & Platforms (2024-2032) ($MN)
  • Table 4 Global Machine Unlearning Solutions Market Outlook, By Standalone Unlearning Software (2024-2032) ($MN)
  • Table 5 Global Machine Unlearning Solutions Market Outlook, By Integrated MLOps/Model Governance Platforms (2024-2032) ($MN)
  • Table 6 Global Machine Unlearning Solutions Market Outlook, By Services (2024-2032) ($MN)
  • Table 7 Global Machine Unlearning Solutions Market Outlook, By Professional Services (2024-2032) ($MN)
  • Table 8 Global Machine Unlearning Solutions Market Outlook, By Consulting & Implementation (2024-2032) ($MN)
  • Table 9 Global Machine Unlearning Solutions Market Outlook, By Support & Maintenance (2024-2032) ($MN)
  • Table 10 Global Machine Unlearning Solutions Market Outlook, By Managed Services / Unlearning-as-a-Service (UaaS) (2024-2032) ($MN)
  • Table 11 Global Machine Unlearning Solutions Market Outlook, By Unlearning Technique (2024-2032) ($MN)
  • Table 12 Global Machine Unlearning Solutions Market Outlook, By Exact Unlearning (2024-2032) ($MN)
  • Table 13 Global Machine Unlearning Solutions Market Outlook, By Approximate Unlearning (2024-2032) ($MN)
  • Table 14 Global Machine Unlearning Solutions Market Outlook, By Influence Function-based Methods (2024-2032) ($MN)
  • Table 15 Global Machine Unlearning Solutions Market Outlook, By Optimization-based Methods (2024-2032) ($MN)
  • Table 16 Global Machine Unlearning Solutions Market Outlook, By Other Approximation Algorithms (2024-2032) ($MN)
  • Table 17 Global Machine Unlearning Solutions Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 18 Global Machine Unlearning Solutions Market Outlook, By Cloud-based (2024-2032) ($MN)
  • Table 19 Global Machine Unlearning Solutions Market Outlook, By On-premises (2024-2032) ($MN)
  • Table 20 Global Machine Unlearning Solutions Market Outlook, By Organization Size (2024-2032) ($MN)
  • Table 21 Global Machine Unlearning Solutions Market Outlook, By Large Enterprises (2024-2032) ($MN)
  • Table 22 Global Machine Unlearning Solutions Market Outlook, By Small and Medium-sized Enterprises (SMEs) (2024-2032) ($MN)
  • Table 23 Global Machine Unlearning Solutions Market Outlook, By Application (2024-2032) ($MN)
  • Table 24 Global Machine Unlearning Solutions Market Outlook, By Bias and Fairness Mitigation (2024-2032) ($MN)
  • Table 25 Global Machine Unlearning Solutions Market Outlook, By Security and Attack Mitigation (2024-2032) ($MN)
  • Table 26 Global Machine Unlearning Solutions Market Outlook, By Compliance with Data Privacy Regulations (2024-2032) ($MN)
  • Table 27 Global Machine Unlearning Solutions Market Outlook, By Model Lifecycle Management and Performance Optimization (2024-2032) ($MN)
  • Table 28 Global Machine Unlearning Solutions Market Outlook, By Other Applications (2024-2032) ($MN)
  • Table 29 Global Machine Unlearning Solutions Market Outlook, By End User (2024-2032) ($MN)
  • Table 30 Global Machine Unlearning Solutions Market Outlook, By BFSI (Banking, Financial Services, and Insurance) (2024-2032) ($MN)
  • Table 31 Global Machine Unlearning Solutions Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 32 Global Machine Unlearning Solutions Market Outlook, By IT & Telecommunications (2024-2032) ($MN)
  • Table 33 Global Machine Unlearning Solutions Market Outlook, By Retail & E-commerce (2024-2032) ($MN)
  • Table 34 Global Machine Unlearning Solutions Market Outlook, By Government & Public Sector (2024-2032) ($MN)
  • Table 35 Global Machine Unlearning Solutions Market Outlook, By Automotive & Manufacturing (2024-2032) ($MN)
  • Table 36 Global Machine Unlearning Solutions Market Outlook, By Media & Entertainment (2024-2032) ($MN)
  • Table 37 Global Machine Unlearning Solutions Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.