查看購物車
  • Simplified Chinese
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1662701

2030 年 ModelOps 市場預測：按產品、部署模式、企業規模、技術、應用、最終用戶和地區進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，全球 ModelOps 市場預計在 2024 年將達到 53.1 億美元，到 2030 年將達到 405.5 億美元，預測期內的複合年成長率為 40.3%。 ModelOps 代表模型操作，是專注於在生產環境中部署、監控、管理和操作人工智慧和機器學習模型的部分。它彌合了資料科學與 IT 營運之間的差距，確保模型按預期運行，同時保持合規性、可擴展性和可靠性。 ModelOps 提供自動化、監控、再培訓和生命週期管理，以簡化模型更新並降低風險。透過專注於管治、審核和效能最佳化，我們幫助組織有效地實施人工智慧並從其模型中獲得一致的商業價值。

監理合規與管治

模型生命週期過程在管治架構的幫助下進行管理，以確保合乎道德的採用並降低風險。強大的 ModelOps 策略對於公司遵守 GDPR 等日益嚴格的資料隱私法規至關重要。隨著監管機構更加重視模型決策的透明度，管治框架至關重要。此外，合規性檢查和審核追蹤對於避免罰款和維護信任至關重要。這些因素共同促使公司投資 ModelOps 解決方案，以最大限度地提高性能並確保其 AI 模型的合規性。

熟練勞動力短缺

公司很難找到具有所需專業知識來管理複雜模型和系統的專家。如果沒有熟練的人才，公司將面臨有效部署、監控和最佳化機器學習模型的挑戰。人才短缺也減緩了 ModelOps 解決方案的採用，從而限制了創新和效率。這種技能差距導致培訓成本上升和對外部供應商的依賴增加。總的來說，無法填補這些角色將會減緩人工智慧和機器學習業務的擴展。

引入 Rising Edge AI

邊緣人工智慧部署的興起將增強模型開發、監控和管理，提高整個產業的效率。 ModelOps 實現資料科學家、IT 團隊和業務領導之間的無縫協作，以加速模型部署。它還促進了大規模自動化模型管理，加快了人工智慧主導解決方案的上市時間。隨著人工智慧系統變得越來越複雜，公司開始轉向 ModelOps 進行持續監控、效能最佳化和管治。因此，合理化的需求日益成長。最終，人工智慧的普及為更快的創新、更大的可擴展性和更好的市場決策奠定了基礎。

技術創新迅速

不斷的調適需求增加了培訓和升級的價格和資源負擔。舊有系統通常與現代技術不相容，從而產生整合問題。快速的技術變化往往導致缺乏標準化，使得公司難以實施一致的程序。此外，維護多個系統的複雜性增加了出現錯誤和效率低下的機會。在如此動盪的市場中，公司很難保持可擴展性和競爭優勢。

COVID-19 的影響

COVID-19 疫情加速了各行各業對人工智慧和機器學習解決方案的採用，對 ModelOps 市場產生了重大影響。組織面臨越來越大的決策自動化和最佳化營運的壓力，從而推動了對強大的模型操作化平台的需求。遠距工作和供應鏈中斷凸顯了對可擴展、敏捷的人工智慧系統的需求，促使企業投資 ModelOps 工具。然而，由於疫情期間某些行業的預算限制，這些解決方案的推出暫時放緩。後疫情時代，隨著企業優先考慮人工智慧主導的轉型以增強韌性和競爭力，市場正在蓬勃發展。

預計預測期內軟體平台部分將實現最大幅度成長。

預計軟體平台部分將在預測期內佔據最大的市場佔有率，因為它能夠簡化人工智慧和機器學習模型的開發、部署和管理。這些平台提供端到端解決方案，以自動化模型生命週期流程、降低操作複雜性並確保可擴展性。監控、再訓練和合規管理等高階功能解決了長期維持模型準確性和可靠性的關鍵挑戰。與現有 IT 生態系統的整合能力加速了採用，使企業更容易大規模實施 AI。此外，它支援各種建模框架和工具的能力可以滿足各種行業的需求並促進廣泛採用。

預計醫療保健和生命科學領域將在預測期內實現最高的複合年成長率。

由於患者治療效果的改善和業務效率的提高，預計醫療保健和生命科學領域在預測期內將呈現最高的成長率。這部分依賴疾病診斷、藥物發現和個人化醫療的預測模型，需要高效的模型部署和監控。 ModelOps 確保遵守嚴格的監管標準，在處理敏感的患者資料時至關重要。電子健康記錄遠端醫療的日益普及加速了對透過 ModelOps 有效管理的強大 AI 模型的需求。此外，該部門專注於臨床決策的即時分析，凸顯了持續更新模型的必要性，從而推動市場成長。

比最大的地區

在預測期內，由於各行業擴大採用人工智慧 (AI) 和機器學習 (ML)，預計亞太地區將佔據最大的市場佔有率。組織正在投資 ModelOps 解決方案，以簡化大規模 AI 模型的部署、監控和管理，確保效率和合規性。對此類解決方案的需求正在成長，特別是在金融、醫療保健和製造等領域，這些領域需要更快、更準確的決策。此外，該地區不斷變化的監管環境以及公共和私營部門的數位轉型動力正在進一步推動市場擴張。在中國、印度和日本等國家的引領下，亞太地區 ModelOps 市場預計將在未來幾年經歷重大的技術進步和成長。

複合年成長率最高的地區

在預測期內，由於對自動化決策流程和業務效率的需求不斷成長，預計南美洲將呈現最高的複合年成長率。巴西、阿根廷和智利是該地區的主要參與企業，專注於將人工智慧模型融入金融、醫療保健和製造業等各個領域。這些國家是科技新興企業和跨國公司的所在地，為 ModelOps 解決方案創造了競爭格局。此外，政府旨在促進數位轉型和人工智慧發展的措施預計將在未來幾年加速市場擴張。

提供免費客製化

訂閱此報告的客戶將獲得以下免費自訂選項之一：

  • 公司簡介
    • 對其他市場參與企業（最多 3 家公司）進行全面分析
    • 主要企業的 SWOT 分析（最多 3 家公司）
  • 地理細分
    • 根據客戶興趣對主要國家進行市場估計、預測和複合年成長率（註：基於可行性檢查）
  • 競爭性基準化分析
    • 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

目錄

第1章執行摘要

第 2 章 前言

  • 概述
  • 相關利益者
  • 研究範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二手研究資料資訊來源
    • 先決條件

第3章 市場走勢分析

  • 介紹
  • 驅動程式
  • 限制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19 的影響

第 4 章 波特五力分析

  • 供應商的議價能力
  • 買家的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

第 5 章。

  • 介紹
  • 軟體平台
  • 服務

6. 以部署模式分類的全球 ModelOps 市場

  • 介紹
  • 本地
  • 雲端基礎

第7章。

  • 介紹
  • 大型企業
  • 中小企業

第 8 章。

  • 介紹
  • 機器學習 (ML)
  • 深度學習（DL）
  • 自然語言處理 (NLP)
  • 預測分析
  • 電腦視覺
  • 強化學習
  • 其他

第9章全球ModelOps 市場依應用分類

  • 介紹
  • 模型開發與訓練
  • 模型部署和操作化
  • 模型監控與管理
  • 模型管治與合規
  • 模型可解釋性和可解釋性
  • 其他

第 10 章。

  • 介紹
  • 銀行、金融服務和保險
  • 醫療保健和生命科學
  • 零售與電子商務
  • 資訊科技和通訊
  • 製造業
  • 能源和公共
  • 政府和公共部門
  • 運輸和物流
  • 媒體和娛樂
  • 教育
  • 其他

第 11 章。

  • 介紹
  • 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
    • 歐洲其他地區
  • 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
    • 南美洲其他地區
  • 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 公司概況

  • 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.