查看购物车
  • Traditional Chinese
  • English
  • Japanese
  • Korean
封面
市场调查报告书
商品编码
1662701

2030 年 ModelOps 市场预测：按产品、部署模式、企业规模、技术、应用、最终用户和地区进行的全球分析

ModelOps Market Forecasts to 2030 - Global Analysis By Offering (Software Platforms and Services), Deployment Mode, Enterprise Size, Technology, 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 ModelOps 市场预计在 2024 年将达到 53.1 亿美元，到 2030 年将达到 405.5 亿美元，预测期内的复合年增长率为 40.3%。 ModelOps 代表模型操作，是专注于在生产环境中部署、监控、管理和操作人工智慧和机器学习模型的部分。它弥合了资料科学与 IT 营运之间的差距，确保模型按预期运行，同时保持合规性、可扩展性和可靠性。 ModelOps 提供自动化、监控、再培训和生命週期管理，以简化模型更新并降低风险。透过专注于管治、审核和效能优化，我们帮助组织有效地实施人工智慧并从其模型中获得一致的商业价值。

监理合规与管治

模型生命週期过程在管治架构的帮助下进行管理，以确保合乎道德的采用并降低风险。强大的 ModelOps 策略对于公司遵守 GDPR 等日益严格的资料隐私法规至关重要。随着监管机构更加重视模型决策的透明度，管治框架至关重要。此外，合规性检查和审核追踪对于避免罚款和维护信任至关重要。这些因素共同促使公司投资 ModelOps 解决方案，以最大限度地提高性能并确保其 AI 模型的合规性。

熟练劳动力短缺

公司很难找到具有所需专业知识来管理复杂模型和系统的专家。如果没有熟练的人才，公司将面临有效部署、监控和优化机器学习模型的挑战。人才短缺也减缓了 ModelOps 解决方案的采用，从而限制了创新和效率。这种技能差距导致培训成本上升和对外部供应商的依赖增加。总的来说，无法填补这些角色将会减缓人工智慧和机器学习业务的扩展。

引入 Rising Edge AI

边缘人工智慧部署的兴起将增强模型开发、监控和管理，提高整个产业的效率。 ModelOps 实现资料科学家、IT 团队和业务领导之间的无缝协作，以加速模型部署。它还促进了大规模自动化模型管理，加快了人工智慧主导解决方案的上市时间。随着人工智慧系统变得越来越复杂，公司开始转向 ModelOps 进行持续监控、效能优化和管治。因此，合理化的需求日益增长。最终，人工智慧的普及为更快的创新、更大的可扩展性和更好的市场决策奠定了基础。

技术创新迅速

不断的调适需求增加了培训和升级的价格和资源负担。旧有系统通常与现代技术不相容，从而产生整合问题。快速的技术变化往往导致缺乏标准化，使得公司难以实施一致的程序。此外，维护多个系统的复杂性增加了出现错误和效率低下的机会。在如此动盪的市场中，公司很难保持可扩展性和竞争优势。

COVID-19 的影响

COVID-19 疫情加速了各行各业对人工智慧和机器学习解决方案的采用，对 ModelOps 市场产生了重大影响。组织面临越来越大的决策自动化和优化营运的压力，从而推动了对强大的模型操作化平台的需求。远距工作和供应链中断凸显了对可扩展、敏捷的人工智慧系统的需求，促使企业投资 ModelOps 工具。然而，由于疫情期间某些行业的预算限制，这些解决方案的推出暂时放缓。后疫情时代，随着企业优先考虑人工智慧主导的转型以增强韧性和竞争力，市场正在蓬勃发展。

预计预测期内软体平台部分将实现最大幅度成长。

预计软体平台部分将在预测期内占据最大的市场占有率，因为它能够简化人工智慧和机器学习模型的开发、部署和管理。这些平台提供端到端解决方案，以自动化模型生命週期流程、降低操作复杂性并确保可扩展性。监控、再训练和合规管理等高阶功能解决了长期维持模型准确性和可靠性的关键挑战。与现有 IT 生态系统的整合能力加速了采用，使企业更容易大规模实施 AI。此外，它支援各种建模框架和工具的能力可以满足各种行业的需求并促进广泛采用。

预计医疗保健和生命科学领域将在预测期内实现最高的复合年增长率。

由于患者治疗效果的改善和业务效率的提高，预计医疗保健和生命科学领域在预测期内将呈现最高的成长率。这部分依赖疾病诊断、药物发现和个人化医疗的预测模型，需要高效的模型部署和监控。 ModelOps 确保遵守严格的监管标准，在处理敏感的患者资料时至关重要。电子健康记录远端医疗的日益普及加速了对透过 ModelOps 有效管理的强大 AI 模型的需求。此外，该部门专注于临床决策的即时分析，凸显了持续更新模型的必要性，从而推动市场成长。

比最大的地区

在预测期内，由于各行业越来越多地采用人工智慧 (AI) 和机器学习 (ML)，预计亚太地区将占据最大的市场占有率。组织正在投资 ModelOps 解决方案，以简化大规模 AI 模型的部署、监控和管理，确保效率和合规性。对此类解决方案的需求正在增长，特别是在金融、医疗保健和製造等领域，这些领域需要更快、更准确的决策。此外，该地区不断变化的监管环境以及公共和私营部门的数位转型动力正在进一步推动市场扩张。在中国、印度和日本等国家的引领下，亚太地区 ModelOps 市场预计将在未来几年经历重大的技术进步和成长。

复合年增长率最高的地区

在预测期内，由于对自动化决策流程和业务效率的需求不断增长，预计南美洲将呈现最高的复合年增长率。巴西、阿根廷和智利是该地区的主要参与企业，专注于将人工智慧模型融入金融、医疗保健和製造业等各个领域。这些国家是科技新兴企业和跨国公司的所在地，为 ModelOps 解决方案创造了竞争格局。此外，政府旨在促进数位转型和人工智慧发展的措施预计将在未来几年加速市场扩张。

提供免费客製化

订阅此报告的客户将获得以下免费自订选项之一：

  • 公司简介
    • 对其他市场参与企业（最多 3 家公司）进行全面分析
    • 主要企业的 SWOT 分析（最多 3 家公司）
  • 地理细分
    • 根据客户兴趣对主要国家进行市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争性基准化分析
    • 根据产品系列、地理分布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 2 章 前言

  • 概述
  • 相关利益者
  • 研究范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二手研究资料资讯来源
    • 先决条件

第三章 市场走势分析

  • 介绍
  • 驱动程式
  • 限制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19 的影响

第 4 章 波特五力分析

  • 供应商的议价能力
  • 买家的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

第 5 章。

  • 介绍
  • 软体平台
  • 服务

6. 以部署模式分類的全球 ModelOps 市场

  • 介绍
  • 本地
  • 云端基础

第七章。

  • 介绍
  • 大型企业
  • 中小企业

第 8 章。

  • 介绍
  • 机器学习 (ML)
  • 深度学习（DL）
  • 自然语言处理 (NLP)
  • 预测分析
  • 电脑视觉
  • 强化学习
  • 其他的

第九章 全球 ModelOps 市场依应用划分

  • 介绍
  • 模型开发与训练
  • 模型部署和操作化
  • 模型监控与管理
  • 模型管治与合规
  • 模型可解释性和可解释性
  • 其他的

第 10 章。

  • 介绍
  • 银行、金融服务和保险
  • 医疗保健和生命科学
  • 零售与电子商务
  • 资讯科技和通讯
  • 製造业
  • 能源和公共
  • 政府和公共部门
  • 运输和物流
  • 媒体和娱乐
  • 教育
  • 其他的

第 11 章。

  • 介绍
  • 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
    • 欧洲其他地区
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 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 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十三章 公司概况

  • IBM Corporation
  • Google
  • Microsoft Corporation
  • Amazon Web Services
  • DataRobot
  • H2O.ai
  • Domino Data Lab
  • Cloudera
  • SAS Institute
  • Alteryx
  • Databricks
  • Algorithmia
  • TIBCO Software
  • RapidMiner
  • CNVRG.io
  • Anaconda
  • C3 AI
  • MathWorks
Product Code: SMRC28562

According to Stratistics MRC, the Global ModelOps Market is accounted for $5.31 billion in 2024 and is expected to reach $40.55 billion by 2030 growing at a CAGR of 40.3% during the forecast period. ModelOps, short for Model Operations, is a discipline focused on deploying, monitoring, managing, and governing AI and machine learning models in production. It bridges the gap between data science and IT operations, ensuring models perform as intended while maintaining compliance, scalability, and reliability. ModelOps involves automation, monitoring, retraining, and lifecycle management to streamline model updates and mitigate risks. It emphasizes governance, auditability, and performance optimization, enabling organizations to operationalize AI effectively and derive consistent business value from their models.

Market Dynamics:

Driver:

Regulatory compliance and governance

Model lifecycle processes are managed with the aid of governance frameworks, which guarantee moral application and reduce hazards. Strong ModelOps strategies are necessary for businesses to stay in compliance with increasingly stringent data privacy regulations, like the GDPR. Governance frameworks are essential since regulatory bodies are placing a greater emphasis on transparency in model decisions. Furthermore, compliance checks and audit trails become crucial for preventing fines and upholding confidence. These elements work together to encourage companies to spend money on ModelOps solutions in order to maximise AI model performance and guarantee compliance.

Restraint:

Lack of skilled workforce

Companies struggle to find professionals with the necessary expertise to manage complex models and systems. Without skilled workers, businesses face challenges in deploying, monitoring, and optimizing machine learning models effectively. The shortage of talent also delays the adoption of ModelOps solutions, limiting innovation and efficiency. This skill gap results in higher training costs and increased reliance on external vendors. Overall, the inability to fill these roles slows down the scaling of AI and machine learning operations.

Opportunity:

Rising edge AI deployments

Rising edge AI deployments enhances model development, monitoring, and management, improving efficiency across industries. ModelOps ensures seamless collaboration between data scientists, IT teams, and business leaders, accelerating model deployment. It also fosters automation in managing models at scale, reducing time-to-market for AI-driven solutions. As AI systems become more complex, businesses are turning to ModelOps for continuous monitoring, performance optimization, and governance. This growing demands for streamlined. Ultimately, the rise of AI deployments is setting the stage for faster innovation, greater scalability, and improved decision-making within the market.

Threat:

Rapid technological changes

The requirement for constant adaptation raises the price and resource commitment for training and upgrades. Integration issues arise because legacy systems frequently become incompatible with modern technologies. Rapid innovation often leads to a lack of standardisation, which makes it challenging for businesses to implement consistent procedures. Furthermore, there is a greater chance of mistakes and inefficiencies due to the complexity of maintaining several systems. It is difficult for businesses to maintain scalability or competitive advantages in this volatile market.

Covid-19 Impact

The COVID-19 pandemic significantly impacted the ModelOps market by accelerating the adoption of AI and machine learning solutions across industries. Organizations faced increased pressure to automate decision-making and optimize operations, driving demand for robust model operationalization platforms. Remote work and disrupted supply chains highlighted the need for scalable and agile AI systems, pushing businesses to invest in ModelOps tools. However, budget constraints in certain sectors during the pandemic slowed down the deployment of these solutions temporarily. Post-pandemic, the market is witnessing rapid growth as enterprises prioritize AI-driven transformation to enhance resilience and competitiveness.

The software platforms segment is expected to be the largest during the forecast period

The software platforms segment is expected to account for the largest market share during the forecast period, by enabling streamlined development, deployment, and management of AI and ML models. These platforms offer end-to-end solutions for automating model lifecycle processes, reducing operational complexities and ensuring scalability. With advanced features like monitoring, retraining, and compliance management, they address critical challenges in maintaining model accuracy and reliability over time. Integration capabilities with existing IT ecosystems enhance adoption, making it easier for organizations to operationalize AI at scale. Additionally, their ability to support diverse modelling frameworks and tools caters to varied industry needs, driving widespread adoption.

The healthcare and life sciences segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the healthcare and life sciences segment is predicted to witness the highest growth rate, due to improved patient outcomes and operational efficiency. This sector relies on predictive models for disease diagnosis, drug discovery, and personalized medicine, necessitating efficient model deployment and monitoring. ModelOps ensures compliance with stringent regulatory standards, critical for handling sensitive patient data. The increasing adoption of electronic health records (EHRs) and telemedicine accelerates the demand for robust AI models, managed effectively through ModelOps. Additionally, the sector's focus on real-time analytics for clinical decision-making emphasizes the need for continuous model updates, thereby propelling the growth of the market.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share due to the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries. Organizations are investing in ModelOps solutions to streamline the deployment, monitoring, and management of AI models at scale, ensuring efficiency and compliance. The need for faster and more accurate decision-making, especially in sectors like finance, healthcare, and manufacturing, is driving demand for these solutions. Additionally, the region's evolving regulatory landscape and the push for digital transformation in both public and private sectors further support the market's expansion. With countries like China, India, and Japan leading the way, the Asia Pacific ModelOps market is poised for significant technological advancements and growth in the coming years.

Region with highest CAGR:

Over the forecast period, the South America region is anticipated to exhibit the highest CAGR, owing to the rising demand for automated decision-making processes and operational efficiency. Brazil, Argentina, and Chile are key players in the region, focusing on integrating AI models into various sectors like finance, healthcare, and manufacturing. The presence of technology startups and multinational companies in these countries is fostering a competitive landscape for ModelOps solutions. Furthermore, government initiatives aimed at promoting digital transformation and AI development are expected to accelerate the market's expansion in the coming years.

Key players in the market

Some of the key players profiled in the ModelOps Market include IBM Corporation, Google, Microsoft Corporation, Amazon Web Services, DataRobot, H2O.ai, Domino Data Lab, Cloudera, SAS Institute, Alteryx, Databricks, Algorithmia, TIBCO Software, RapidMiner, CNVRG.io, Anaconda, C3 AI and MathWorks.

Key Developments:

In October 2024, IBM launched "Granite 3.0," the latest version of its artificial intelligence models tailored for businesses. These models are open-source, distinguishing IBM from competitors like Microsoft, which charge for access to their AI models.

In July 2024, Google Cloud announced a partnership with Mistral AI to integrate its Codestral AI model into Google's Vertex AI service. This collaboration introduced Codestral, a generative AI model designed specifically for code generation tasks, as a fully-managed service within Vertex AI.

In February 2024, IBM and Wipro announced an expansion of their partnership to deliver new AI services. Wipro introduced the Enterprise AI-Ready Platform, leveraging IBM's watsonx AI and data platform, including watsonx.ai, watsonx.data, and watsonx.governance.

Offerings Covered:

  • Software Platforms
  • Services

Deployment Modes Covered:

  • On-Premises
  • Cloud-Based

Enterprise Sizes Covered:

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

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning (DL)
  • Natural Language Processing (NLP)
  • Predictive Analytics
  • Computer Vision
  • Reinforcement Learning
  • Other Technologies

Applications Covered:

  • Model Development and Training
  • Model Deployment and Operationalization
  • Model Monitoring and Management
  • Model Governance and Compliance
  • Model Explainability and Interpretability
  • Other Applications

End Users Covered:

  • Banking, Financial Services, and Insurance
  • Healthcare and Life Sciences
  • Retail and E-Commerce
  • IT and Telecommunications
  • Manufacturing
  • Energy and Utilities
  • Government and Public Sector
  • Transportation and Logistics
  • Media and Entertainment
  • Education
  • 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 2022, 2023, 2024, 2026, and 2030
  • 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 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 ModelOps Market, By Offering

  • 5.1 Introduction
  • 5.2 Software Platforms
  • 5.3 Services

6 Global ModelOps Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 On-Premises
  • 6.3 Cloud-Based

7 Global ModelOps Market, By Enterprise Size

  • 7.1 Introduction
  • 7.2 Large Enterprises
  • 7.3 Small and Medium-Sized Enterprises (SMEs)

8 Global ModelOps Market, By Technology

  • 8.1 Introduction
  • 8.2 Machine Learning (ML)
  • 8.3 Deep Learning (DL)
  • 8.4 Natural Language Processing (NLP)
  • 8.5 Predictive Analytics
  • 8.6 Computer Vision
  • 8.7 Reinforcement Learning
  • 8.8 Other Technologies

9 Global ModelOps Market, By Application

  • 9.1 Introduction
  • 9.2 Model Development and Training
  • 9.3 Model Deployment and Operationalization
  • 9.4 Model Monitoring and Management
  • 9.5 Model Governance and Compliance
  • 9.6 Model Explainability and Interpretability
  • 9.7 Other Applications

10 Global ModelOps Market, By End User

  • 10.1 Introduction
  • 10.2 Banking, Financial Services, and Insurance
  • 10.3 Healthcare and Life Sciences
  • 10.4 Retail and E-Commerce
  • 10.5 IT and Telecommunications
  • 10.6 Manufacturing
  • 10.7 Energy and Utilities
  • 10.8 Government and Public Sector
  • 10.9 Transportation and Logistics
  • 10.10 Media and Entertainment
  • 10.11 Education
  • 10.12 Other End Users

11 Global ModelOps 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 IBM Corporation
  • 13.2 Google
  • 13.3 Microsoft Corporation
  • 13.4 Amazon Web Services
  • 13.5 DataRobot
  • 13.6 H2O.ai
  • 13.7 Domino Data Lab
  • 13.8 Cloudera
  • 13.9 SAS Institute
  • 13.10 Alteryx
  • 13.11 Databricks
  • 13.12 Algorithmia
  • 13.13 TIBCO Software
  • 13.14 RapidMiner
  • 13.15 CNVRG.io
  • 13.16 Anaconda
  • 13.17 C3 AI
  • 13.18 MathWorks

List of Tables

  • Table 1 Global ModelOps Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global ModelOps Market Outlook, By Offering (2022-2030) ($MN)
  • Table 3 Global ModelOps Market Outlook, By Software Platforms (2022-2030) ($MN)
  • Table 4 Global ModelOps Market Outlook, By Services (2022-2030) ($MN)
  • Table 5 Global ModelOps Market Outlook, By Deployment Mode (2022-2030) ($MN)
  • Table 6 Global ModelOps Market Outlook, By On-Premises (2022-2030) ($MN)
  • Table 7 Global ModelOps Market Outlook, By Cloud-Based (2022-2030) ($MN)
  • Table 8 Global ModelOps Market Outlook, By Enterprise Size (2022-2030) ($MN)
  • Table 9 Global ModelOps Market Outlook, By Large Enterprises (2022-2030) ($MN)
  • Table 10 Global ModelOps Market Outlook, By Small and Medium-Sized Enterprises (SMEs) (2022-2030) ($MN)
  • Table 11 Global ModelOps Market Outlook, By Technology (2022-2030) ($MN)
  • Table 12 Global ModelOps Market Outlook, By Machine Learning (ML) (2022-2030) ($MN)
  • Table 13 Global ModelOps Market Outlook, By Deep Learning (DL) (2022-2030) ($MN)
  • Table 14 Global ModelOps Market Outlook, By Natural Language Processing (NLP) (2022-2030) ($MN)
  • Table 15 Global ModelOps Market Outlook, By Predictive Analytics (2022-2030) ($MN)
  • Table 16 Global ModelOps Market Outlook, By Computer Vision (2022-2030) ($MN)
  • Table 17 Global ModelOps Market Outlook, By Reinforcement Learning (2022-2030) ($MN)
  • Table 18 Global ModelOps Market Outlook, By Other Technologies (2022-2030) ($MN)
  • Table 19 Global ModelOps Market Outlook, By Application (2022-2030) ($MN)
  • Table 20 Global ModelOps Market Outlook, By Model Development and Training (2022-2030) ($MN)
  • Table 21 Global ModelOps Market Outlook, By Model Deployment and Operationalization (2022-2030) ($MN)
  • Table 22 Global ModelOps Market Outlook, By Model Monitoring and Management (2022-2030) ($MN)
  • Table 23 Global ModelOps Market Outlook, By Model Governance and Compliance (2022-2030) ($MN)
  • Table 24 Global ModelOps Market Outlook, By Model Explainability and Interpretability (2022-2030) ($MN)
  • Table 25 Global ModelOps Market Outlook, By Other Applications (2022-2030) ($MN)
  • Table 26 Global ModelOps Market Outlook, By End User (2022-2030) ($MN)
  • Table 27 Global ModelOps Market Outlook, By Banking, Financial Services, and Insurance (2022-2030) ($MN)
  • Table 28 Global ModelOps Market Outlook, By Healthcare and Life Sciences (2022-2030) ($MN)
  • Table 29 Global ModelOps Market Outlook, By Retail and E-Commerce (2022-2030) ($MN)
  • Table 30 Global ModelOps Market Outlook, By IT and Telecommunications (2022-2030) ($MN)
  • Table 31 Global ModelOps Market Outlook, By Manufacturing (2022-2030) ($MN)
  • Table 32 Global ModelOps Market Outlook, By Energy and Utilities (2022-2030) ($MN)
  • Table 33 Global ModelOps Market Outlook, By Government and Public Sector (2022-2030) ($MN)
  • Table 34 Global ModelOps Market Outlook, By Transportation and Logistics (2022-2030) ($MN)
  • Table 35 Global ModelOps Market Outlook, By Media and Entertainment (2022-2030) ($MN)
  • Table 36 Global ModelOps Market Outlook, By Education (2022-2030) ($MN)
  • Table 37 Global ModelOps Market Outlook, By Other End Users (2022-2030) ($MN)

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