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1916692

全球人工智慧模型監控和生命週期管理市場：預測至 2032 年——按組件、生命週期階段、分析類型、部署方法、最終用戶和地區分類的分析

AI Model Monitoring & Lifecycle Management Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Lifecycle Stage, Analytics Type, Deployment Model, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，全球人工智慧模型監控和生命週期管理市場預計到 2025 年將達到 12.94 億美元，到 2032 年將達到 176.932 億美元，在預測期內複合年成長率為 45.3%。

「人工智慧模型監控與生命週期管理」是指對人工智慧模式從開發、部署到退役的整個生命週期進行持續的監控、維護和治理。它即時追蹤模型的性能、準確性、偏差、漂移、可解釋性和合規性，以確保模型在不斷變化的環境中按預期運行。生命週期管理包括模型的訓練、檢驗、版本控制、部署、更新、重新訓練和退役。透過統籌實施這些工作，組織可以快速識別問題、最佳化效能、管理風險，並確保在模型的整個生命週期內符合監管、業務和營運要求，從而維護值得信賴、符合倫理且擴充性的人工智慧系統。

對健全的人工智慧管治框架的需求日益成長

為了確保自動化決策的透明度、公平性和合規性，各組織機構越來越需要結構化的監督機制。生命週期管理平台能夠持續監控模型的效能、偏差和漂移。供應商正在設計以管治為中心的解決方案，這些方案整合了可解釋性、審核追蹤和合規性儀錶板。對可信賴人工智慧系統日益成長的需求正在加速金融、醫療保健和公共管理等受監管行業的採用。這種對管治使得監督工具成為負責任地採用人工智慧的核心支柱。

熟練的人工智慧維運人員短缺

熟練的人工智慧運維人才短缺仍然是市場成長的一大障礙。許多企業難以招募到具備多層級維運（MLOps）和生命週期管理專業知識的人才。與擁有完善培訓體系和資源的成熟企業相比，中小企業面臨的挑戰更大。管理多模型環境的複雜性加劇了技能差距。供應商正在積極採用自動化和低程式碼平台，以減少對專業知識的依賴。儘管採取了這些措施，人才短缺仍然是人工智慧廣泛應用的一大障礙。

擴展自動化模型重訓練工具

對自適應模型的日益依賴推動了對能夠根據不斷變化的資料集進行自我更新的工具的需求。持續的重新訓練可以提高準確性、減少偏差並增強預測置信度。供應商正在將機器學習管道整合到其監控平台中，以簡化重新訓練工作流程。對自動化領域不斷成長的投資正在推動零售、醫療保健和物流等行業的需求。重新訓練工具的普及正在將生命週期管理從被動監控轉變為主動最佳化。

超越控制的模型快速演變

模型演進速度遠遠超出合規框架的更新速度，這導致偏差、偏差和違規風險。與大型企業相比，小規模供應商缺乏持續更新監控系統的資源。監管機構正加強對未能建立適應模型演進的管治的人工智慧系統的審查。模型演進的快速發展使得自適應控制成為永續人工智慧部署的關鍵。

新冠疫情的感染疾病：

新冠疫情加速了對模型監控的需求，因為企業正在擴大人工智慧在危機應變工作管理中的應用。然而，快速普及也帶來了偏見、缺乏透明度和違規的風險。同時，醫療保健、物流和公共服務領域對人工智慧的依賴日益增強，也增加了對監控框架的需求。企業部署了漂移偵測和重新訓練工具，以在動盪的環境下保持模型的準確性。供應商則整合了可解釋性和合規性功能，以增強模型的可靠性。疫情凸顯了監控的重要性，它如同一個保障機制，在不確定的環境中平衡創新與課責。

預計在預測期內，模型監測和漂移檢測領域將佔據最大的市場佔有率。

在預測期內，模型監控和漂移檢測領域預計將佔據最大的市場佔有率，這主要得益於對人工智慧效能持續監控的需求。漂移檢測工具能夠幫助企業識別準確性和公平性方面的偏差。供應商正在將即時監控功能整合到其工作流程中，以增強合規性和信任度。受監管行業對透明度的日益成長的需求正在推動該領域的應用。企業認為監控對於維護信任和業務連續性至關重要。漂移偵測的主導地位凸顯了其作為人工智慧生命週期管理基礎的重要角色。

預計在預測期內，醫療和生命科學產業將呈現最高的複合年成長率。

在預測期內，醫療保健和生命科學領域預計將保持最高的成長率，這主要得益於患者照護和藥物研發領域對符合倫理規範的人工智慧日益成長的需求。醫療服務提供者越來越需要監管框架，以確保診斷和預測模型的透明度。供應商正在將偏差檢測、可解釋性和重新訓練功能整合到其醫療保健人工智慧平台中。從中小企業到大型機構，都能從與其醫療保健數據和監管要求相匹配的可擴展監管中受益。對數位健康生態系統的不斷增加的投資也進一步推動了該領域的需求。

佔比最大的地區：

由於法規結構成熟且企業對人工智慧監控的廣泛應用，預計北美將在預測期內保持最大的市場佔有率。美國和加拿大公司在投資合主導平台方面處於主導，以滿足聯邦和州政府的監管要求。領先技術提供商的存在進一步鞏固了該地區的市場主導地位。金融、醫療保健和公共服務領域對符合倫理規範的人工智慧的需求不斷成長，正在推動其應用。供應商正在推出先進的審核和監控功能，以在競爭激烈的市場中脫穎而出。

複合年成長率最高的地區：

預計亞太地區在預測期內將實現最高的複合年成長率，這主要得益於快速的數位化、人工智慧應用的日益普及以及政府主導的人工智慧倫理舉措。中國、印度和東南亞等國家正大力投資監控框架，以支持其人工智慧主導的成長。當地企業正在部署重新訓練和漂移檢測工具，以增強擴充性並滿足監管要求。Start-Ups和區域供應商正在推出針對不同市場量身定做的、具有成本效益的監控解決方案。政府推行的促進負責任的人工智慧和資料保護的計畫正在加速這些技術的應用。亞太地區的成長軌跡以其快速擴展監控技術創新的能力為特徵，使其成為全球成長最快的人工智慧生命週期管理中心。

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  • 公司簡介
    • 對最多三家其他公司進行全面分析
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  • 區域分類
    • 根據客戶興趣對主要國家進行市場估算、預測和複合年成長率分析（註：基於可行性檢查）
  • 競爭標竿分析
    • 基於產品系列、地域覆蓋和策略聯盟對主要企業進行基準分析

目錄

第1章執行摘要

第2章 引言

  • 概述
  • 相關利益者
  • 分析範圍
  • 分析方法
  • 分析材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代產品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球人工智慧模型監控與生命週期管理市場（按組件分類）

  • 軟體
    • 模型監測平台
    • 性能和漂移檢測
    • 偏見與公平性審核
    • 問責制和透明度框架
  • 服務
    • 諮詢
    • 整合與實施
    • 託管服務

6. 全球人工智慧模型監控與生命週期管理市場（依生命週期階段分類）

  • 模型開發與培訓
  • 模型實現與整合
  • 模型監測和漂移檢測
  • 模型重訓練與最佳化
  • 退休模式與管治
  • 其他生命週期階段

7. 全球人工智慧模型監控與生命週期管理市場依分析類型分類

  • 預測性監測
  • 處方監測
  • 可解釋人工智慧監控

8. 全球人工智慧模型監控與生命週期管理市場（以部署方式分類）

  • 本地部署
  • 雲

9. 全球人工智慧模型監控和生命週期管理市場（按最終用戶分類）

  • 銀行、金融服務和保險（BFSI）
  • 醫學與生命科​​學
  • 零售與電子商務
  • 資訊科技/通訊
  • 政府/公共部門
  • 媒體與娛樂
  • 其他最終用戶

第10章：全球人工智慧模型監控與生命週期管理市場（按地區分類）

  • 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
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 亞太其他地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 其他南美國家
  • 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11章：主要趨勢

  • 合約、商業夥伴關係和合資企業
  • 企業合併（M&A）
  • 新產品上市
  • 業務拓展
  • 其他關鍵策略

第12章：公司簡介

  • IBM Corporation
  • Microsoft Corporation
  • Google Cloud
  • Amazon Web Services, Inc.
  • Salesforce, Inc.
  • SAP SE
  • Oracle Corporation
  • Accenture plc
  • Deloitte Touche Tohmatsu Limited
  • PricewaterhouseCoopers International Limited
  • Ernst &Young Global Limited
  • KPMG International Limited
  • DataRobot, Inc.
  • Fiddler AI, Inc.
  • Arthur AI, Inc.
Product Code: SMRC33265

According to Stratistics MRC, the Global AI Model Monitoring & Lifecycle Management Market is accounted for $1294.0 million in 2025 and is expected to reach $17693.2 million by 2032 growing at a CAGR of 45.3% during the forecast period. AI Model Monitoring & Lifecycle Management refers to the continuous oversight, maintenance, and governance of artificial intelligence models from development through deployment and retirement. It involves tracking model performance, accuracy, bias, drift, explainability, and compliance in real time to ensure models operate as intended in changing environments. Lifecycle management includes model training, validation, versioning, deployment, updating, retraining, and decommissioning. Together, these practices help organizations maintain reliable, ethical, and scalable AI systems by quickly identifying issues, optimizing performance, managing risk, and ensuring alignment with regulatory, business, and operational requirements throughout the model's lifespan.

Market Dynamics:

Driver:

Rising demand for robust AI governance frameworks

Organizations increasingly need structured oversight to guarantee transparency, fairness, and compliance in automated decision-making. Lifecycle management platforms provide continuous visibility into model performance, bias, and drift. Vendors are designing governance-focused solutions that integrate explainability, audit trails, and compliance dashboards. Growing demand for reliable AI systems is accelerating adoption across regulated sectors such as finance, healthcare, and public administration. The emphasis on governance is positioning monitoring tools as a central pillar of responsible AI deployment.

Restraint:

Shortage of skilled AI operations talent

A shortage of skilled AI operations talent remains a significant barrier to market growth. Many organizations struggle to recruit professionals with expertise in MLOps and lifecycle management. Smaller firms face greater challenges compared to incumbents with established training programs and resources. The complexity of managing multi-model environments further intensifies the skills gap. Vendors are introducing automation and low-code platforms to reduce reliance on specialized expertise. Despite these measures the talent deficit remains a critical obstacle to scaling adoption.

Opportunity:

Expansion of automated model retraining tools

Growing reliance on adaptive models is driving demand for tools that update themselves with evolving datasets. Continuous retraining improves accuracy, reduces bias, and enhances predictive reliability. Vendors are embedding machine learning pipelines into monitoring platforms to streamline retraining workflows. Rising investment in automation is boosting demand across industries such as retail, healthcare, and logistics. The expansion of retraining tools is transforming lifecycle management from reactive oversight into proactive optimization.

Threat:

Rapid model evolution outpacing controls

Models are changing faster than compliance frameworks can adapt. This creates risks of bias, drift, and regulatory breaches. Smaller providers lack the resources to continuously update monitoring systems compared to larger incumbents. Regulators are intensifying scrutiny on AI systems that fail to adapt governance to evolving models. The pace of model evolution is making adaptive controls essential for sustainable AI deployment.

Covid-19 Impact:

The Covid-19 pandemic accelerated demand for model monitoring as enterprises scaled AI to manage crisis-driven workloads. Rapid adoption, however, introduced risks of bias, transparency gaps, and compliance breaches. At the same time, reliance on AI in healthcare, logistics, and public services increased demand for monitoring frameworks. Enterprises turned to drift detection and retraining tools to maintain accuracy during volatile conditions. Vendors integrated explainability and compliance features to strengthen trust. The pandemic underscored monitoring as a safeguard for balancing innovation with accountability in uncertain environments.

The model monitoring & drift detection segment is expected to be the largest during the forecast period

The model monitoring & drift detection segment is expected to account for the largest market share during the forecast period, driven by demand for continuous oversight of AI performance. Drift detection tools allow enterprises to identify deviations in accuracy and fairness. Vendors are embedding real-time monitoring into workflows to strengthen compliance and reliability. Rising demand for transparency in regulated industries is boosting adoption in this segment. Enterprises view monitoring as critical for sustaining trust and operational resilience. The dominance of drift detection highlights its role as the backbone of AI lifecycle management.

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

Over the forecast period, the healthcare & life sciences segment is predicted to witness the highest growth rate, supported by rising demand for ethical AI in patient care and drug development. Healthcare providers increasingly require monitoring frameworks to ensure transparency in diagnostic and predictive models. Vendors are embedding bias detection, explainability, and retraining features into healthcare AI platforms. Both SMEs and large institutions benefit from scalable monitoring tailored to medical data and regulatory mandates. Rising investment in digital health ecosystems is amplifying demand in this segment.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share by mature regulatory frameworks and strong enterprise adoption of AI monitoring. Enterprises in the United States and Canada are leading investments in compliance-driven platforms to align with federal and state mandates. The presence of major technology providers further strengthens regional dominance. Rising demand for ethical AI in finance, healthcare, and public services is boosting adoption. Vendors are deploying advanced audit and monitoring features to differentiate offerings in competitive markets.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid digitalization, expanding AI adoption, and government-led ethical AI initiatives. Countries such as China, India, and Southeast Asia are investing heavily in monitoring frameworks to support AI-driven growth. Local enterprises are adopting retraining and drift detection tools to strengthen scalability and meet regulatory expectations. Startups and regional vendors are deploying cost-effective monitoring solutions tailored to diverse markets. Government programs promoting responsible AI and data protection are accelerating adoption. Asia Pacific's trajectory is defined by its ability to scale monitoring innovation quickly, positioning it as the fastest-growing hub for AI lifecycle management worldwide.

Key players in the market

Some of the key players in AI Model Monitoring & Lifecycle Management Market include IBM Corporation, Microsoft Corporation, Google Cloud, Amazon Web Services, Inc., Salesforce, Inc., SAP SE, Oracle Corporation, Accenture plc, Deloitte Touche Tohmatsu Limited, PricewaterhouseCoopers International Limited, Ernst & Young Global Limited, KPMG International Limited, DataRobot, Inc., Fiddler AI, Inc. and Arthur AI, Inc.

Key Developments:

In October 2024, Google Cloud and Accenture expanded their partnership to launch the "Accenture Google Cloud AI Center of Excellence," focusing on responsible AI implementation. This initiative directly includes developing frameworks and tools for managing and monitoring AI model lifecycles for enterprise clients.

In November 2023, AWS and Databricks announced a strategic collaboration to accelerate data and AI governance. This integration allows customers to use Databricks' Unity Catalog with Amazon SageMaker, providing centralized access control, auditing, and lineage tracking for AI models built on AWS.

Components Covered:

  • Software
  • Services

Lifecycle Stages Covered:

  • Model Development & Training
  • Model Deployment & Integration
  • Model Monitoring & Drift Detection
  • Model Retraining & Optimization
  • Model Retirement & Governance
  • Other Lifecycle Stages

Analytics Types Covered:

  • Predictive Monitoring
  • Prescriptive Monitoring
  • Explainable AI Monitoring

Deployment Models Covered:

  • On-Premise
  • Cloud

End Users Covered:

  • Banking, Financial Services & Insurance (BFSI)
  • Healthcare & Life Sciences
  • Retail & E-Commerce
  • IT & Telecommunications
  • Government & Public Sector
  • 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 End User Analysis
  • 3.7 Emerging Markets
  • 3.8 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 AI Model Monitoring & Lifecycle Management Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Model Monitoring Platforms
    • 5.2.2 Performance & Drift Detection
    • 5.2.3 Bias & Fairness Auditing
    • 5.2.4 Explainability & Transparency Frameworks
  • 5.3 Services
    • 5.3.1 Consulting
    • 5.3.2 Integration & Implementation
    • 5.3.3 Managed Services

6 Global AI Model Monitoring & Lifecycle Management Market, By Lifecycle Stage

  • 6.1 Introduction
  • 6.2 Model Development & Training
  • 6.3 Model Deployment & Integration
  • 6.4 Model Monitoring & Drift Detection
  • 6.5 Model Retraining & Optimization
  • 6.6 Model Retirement & Governance
  • 6.7 Other Lifecycle Stages

7 Global AI Model Monitoring & Lifecycle Management Market, By Analytics Type

  • 7.1 Introduction
  • 7.2 Predictive Monitoring
  • 7.3 Prescriptive Monitoring
  • 7.4 Explainable AI Monitoring

8 Global AI Model Monitoring & Lifecycle Management Market, By Deployment Model

  • 8.1 Introduction
  • 8.2 On-Premise
  • 8.3 Cloud

9 Global AI Model Monitoring & Lifecycle Management Market, By End User

  • 9.1 Introduction
  • 9.2 Banking, Financial Services & Insurance (BFSI)
  • 9.3 Healthcare & Life Sciences
  • 9.4 Retail & E-Commerce
  • 9.5 IT & Telecommunications
  • 9.6 Government & Public Sector
  • 9.7 Media & Entertainment
  • 9.8 Other End Users

10 Global AI Model Monitoring & Lifecycle Management Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM Corporation
  • 12.2 Microsoft Corporation
  • 12.3 Google Cloud
  • 12.4 Amazon Web Services, Inc.
  • 12.5 Salesforce, Inc.
  • 12.6 SAP SE
  • 12.7 Oracle Corporation
  • 12.8 Accenture plc
  • 12.9 Deloitte Touche Tohmatsu Limited
  • 12.10 PricewaterhouseCoopers International Limited
  • 12.11 Ernst & Young Global Limited
  • 12.12 KPMG International Limited
  • 12.13 DataRobot, Inc.
  • 12.14 Fiddler AI, Inc.
  • 12.15 Arthur AI, Inc.

List of Tables

  • Table 1 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Model Monitoring Platforms (2024-2032) ($MN)
  • Table 5 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Performance & Drift Detection (2024-2032) ($MN)
  • Table 6 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Bias & Fairness Auditing (2024-2032) ($MN)
  • Table 7 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Explainability & Transparency Frameworks (2024-2032) ($MN)
  • Table 8 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Services (2024-2032) ($MN)
  • Table 9 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Consulting (2024-2032) ($MN)
  • Table 10 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Integration & Implementation (2024-2032) ($MN)
  • Table 11 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Managed Services (2024-2032) ($MN)
  • Table 12 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Lifecycle Stage (2024-2032) ($MN)
  • Table 13 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Model Development & Training (2024-2032) ($MN)
  • Table 14 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Model Deployment & Integration (2024-2032) ($MN)
  • Table 15 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Model Monitoring & Drift Detection (2024-2032) ($MN)
  • Table 16 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Model Retraining & Optimization (2024-2032) ($MN)
  • Table 17 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Model Retirement & Governance (2024-2032) ($MN)
  • Table 18 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Other Lifecycle Stages (2024-2032) ($MN)
  • Table 19 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Analytics Type (2024-2032) ($MN)
  • Table 20 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Predictive Monitoring (2024-2032) ($MN)
  • Table 21 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Prescriptive Monitoring (2024-2032) ($MN)
  • Table 22 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Explainable AI Monitoring (2024-2032) ($MN)
  • Table 23 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 24 Global AI Model Monitoring & Lifecycle Management Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 25 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 26 Global AI Model Monitoring & Lifecycle Management Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Banking, Financial Services & Insurance (BFSI) (2024-2032) ($MN)
  • Table 28 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Healthcare & Life Sciences (2024-2032) ($MN)
  • Table 29 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 30 Global AI Model Monitoring & Lifecycle Management Market Outlook, By IT & Telecommunications (2024-2032) ($MN)
  • Table 31 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Government & Public Sector (2024-2032) ($MN)
  • Table 32 Global AI Model Monitoring & Lifecycle Management Market Outlook, By Media & Entertainment (2024-2032) ($MN)
  • Table 33 Global AI Model Monitoring & Lifecycle Management 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.