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市场调查报告书
商品编码
1916693

全球机器学习维运平台市场：预测至 2032 年 - 按组件、机器学习框架支援、部署方法、生命週期阶段、最终用户和地区进行分析

MLOps Platforms Market Forecasts to 2032 - Global Analysis By Component (Software and Services), ML Framework Support, Deployment Model, Lifecycle Stage, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球 MLOps 平台市场价值将达到 18.5 亿美元，到 2032 年将达到 214.9 亿美元，在预测期内的复合年增长率将达到 42%。

MLOps平台是整合式软体解决方案，使组织能够以可扩展、管治和可控的方式管理机器学习模型的端到端生命週期。它们在一个统一的框架内整合了资料准备、模型开发、训练、测试、部署、监控和重新训练等工具。 MLOps平台支援资料科学家、工程师和IT团队之间的协作，同时确保版本控制、可重复性、安全性和合规性。透过工作流程自动化和对模型效能及偏差的持续监控，这些平台帮助企业有效率地部署机器学习，缩短生产週期，并在各种环境中维护可靠、高品质的AI系统。

可扩展模型部署自动化的需求

企业面临越来越大的压力，需要在各种环境中快速部署人工智慧。 MLOps 平台能够简化大规模模式部署、监控和管治。供应商正在整合编配和自动化功能，以减少人工干预。对效率和速度日益增长的需求正在加速金融、医疗保健和零售等行业的采用。对可扩展部署自动化的需求使 MLOps 平台成为企业人工智慧策略的关键驱动力。

与旧有系统的复杂集成

企业在将现代化工作流程与过时的IT基础设施相容方面面临许多挑战。与拥有成熟现代化预算的大型企业相比，中小企业面临的挑战更大。多供应商系统之间缺乏互通性，进一步加剧了延误。供应商正在引入模组化框架和API以减轻整合负担。持续的复杂性减缓了采用速度，因此相容性成为MLOps平台扩展的关键因素。

边缘人工智慧和物联网的应用日益广泛

边缘人工智慧和物联网的日益普及为机器学习运维（MLOps）供应商创造了巨大的成长机会。互联设备的激增推动了对边缘环境模型管理平台的需求。即时监控和重新训练功能增强了模型在动态环境中的反应能力。供应商正在整合轻量级编配工具以支援分散式部署。对物联网生态系统的投资正在推动可扩展MLOps框架的需求。边缘人工智慧和物联网的融合正在重新定义MLOps，使其成为分散式智慧的促进者。

资料隐私和监管挑战

企业在处理敏感个人和财务数据的AI系统方面面临日益严格的审查。与拥有更雄厚资源的现有企业相比，小规模的供应商更难维持合规性。区域法规结构也增加了部署策略的复杂性。供应商正在整合加密和匿名化功能以增强信任。日益增长的监管负担正在重塑优先事项，使隐私保护成为MLOps成功的核心要素。

新冠疫情的影响：

新冠疫情加速了对机器学习运维（MLOps）平台的需求，因为企业正在扩展人工智慧（AI）应用以管理危机应变工作。同时，供应链中断导致基础设施计划延期，现代化进程受阻。此外，医疗保健、物流和零售业对人工智慧的日益依赖推动了MLOps框架的普及。企业越来越依赖自动化监控和再训练来确保在动盪环境下的准确性。供应商则在平台中加入了可解释性和合规性功能，以增强可靠性。疫情凸显了MLOps平台在不确定环境中平衡创新与课责的迫切需求。

预计在预测期内，软体领域将占据最大的市场份额。

在预测期内，软体领域预计将占据最大的市场份额，这主要得益于对能够简化部署和监控的平台的需求。企业正在将基于软体的编配整合到人工智慧工作流程中，以提高可扩展性和合规性。供应商正在开发整合自动化、再培训和管治功能的解决方案。受监管行业对效率日益增长的需求正在加速该领域的应用。企业将软体平台视为维持营运弹性和可靠性的关键。软体的主导地位反映了其作为MLOps生态系统基础的角色。

在预测期内，模型重训练部分将呈现最高的复合年增长率。

受自适应人工智慧系统需求不断增长的推动，模型重训练领域预计将在预测期内实现最高成长率。企业越来越需要重训练框架来适应不断变化的资料集并保持模型精确度。为了提高应对力，供应商正在将自动化重训练流程整合到其MLOps平台中。从中小企业到大型企业，各行各业都能受益于可扩展的、针对不同行业量身定制的重训练方案。对人工智慧驱动的自动化领域不断增长的投资正在推动该领域的需求。模型重训练的成长凸显了将MLOps重新定义为一种主动最佳化工具的重要性。

占比最大的地区：

由于成熟的人工智慧基础设施和MLOps平台在企业中的广泛应用，预计北美将在预测期内保持最大的市场份额。美国和加拿大的公司在投资主导规框架以满足监管要求方面主导。主要技术提供商的存在进一步巩固了该地区的领先地位。可扩展人工智慧部署的需求不断增长，正在推动各行业的应用。供应商正在整合先进的编配和监控功能，以在竞争激烈的市场中脱颖而出。

复合年增长率最高的地区：

预计亚太地区在预测期内将实现最高的复合年增长率，这主要得益于快速的数位化、人工智慧的日益普及以及政府主导的创新倡议。中国、印度和东南亚等国家正大力投资机器学习维运（MLOps）平台，以支援其人工智慧主导的成长。当地企业正在采用重新训练和编配工具来增强扩充性并满足监管要求。Start-Ups和区域供应商正在推出针对不同市场量身定制的高性价比解决方案。政府推动数位转型和人工智慧应用的计画正在加速市场需求。亚太地区的成长轨迹以其快速扩展创新规模的能力为特征，使其成为全球成长最快的机器学习维运平台中心。

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）
  • 竞争标竿分析
    • 基于产品系列、地域覆盖和策略联盟对主要企业进行基准分析

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
  • 分析材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的影响

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
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球 MLOps 平台市场（按组件划分）

  • 软体
    • 模型开发平台
    • 模型部署工具
    • 监测和漂移检测
    • 偏见与公平工具
  • 服务
    • 模型开发平台
    • 模型部署工具
    • 监测和漂移检测
    • 偏见与公平工具

6. 全球MLOps平台市场（以机器学习架构支援划分）

  • TensorFlow
  • PyTorch
  • Scikit-learn
  • XGBoost
  • 支援多种框架
  • 其他机器学习框架支持

7. 全球MLOps平台市场依部署方式划分

  • 本地部署
  • 云

8. 全球MLOps平台市场依生命週期阶段划分

  • 模型开发
  • 模型部署
  • 模型监测
  • 模型重新训练
  • 模范退休
  • 其他生命週期阶段

9. 全球 MLOps 平台市场（依最终用户划分）

  • BFSI
  • 医疗保健
  • 零售与电子商务
  • 资讯科技/通讯
  • 製造业
  • 能源与公用事业
  • 政府
  • 媒体与娱乐
  • 其他最终用户

第十章：全球MLOps平台市场（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

第十一章：主要趋势

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

第十二章：公司简介

  • IBM Corporation
  • Microsoft Corporation
  • Google Cloud
  • Amazon Web Services, Inc.
  • Salesforce, Inc.
  • SAP SE
  • Oracle Corporation
  • DataRobot, Inc.
  • Fiddler AI, Inc.
  • Arthur AI, Inc.
  • H2O.ai, Inc.
  • Domino Data Lab, Inc.
  • Weights &Biases, Inc.
  • Intel Corporation
  • Allegro AI, Inc.
Product Code: SMRC33266

According to Stratistics MRC, the Global MLOps Platforms Market is accounted for $1.85 billion in 2025 and is expected to reach $21.49 billion by 2032 growing at a CAGR of 42% during the forecast period. MLOps platforms are integrated software solutions that enable organizations to manage the end-to-end lifecycle of machine learning models in a scalable, automated, and governed manner. They combine tools for data preparation, model development, training, testing, deployment, monitoring, and retraining within a unified framework. MLOps platforms support collaboration between data scientists, engineers, and IT teams while ensuring version control, reproducibility, security, and compliance. By automating workflows and continuously monitoring model performance and drift, these platforms help enterprises operationalize machine learning efficiently, reduce time to production, and maintain reliable, high-quality AI systems across diverse environments.

Market Dynamics:

Driver:

Demand for scalable model deployment automation

Organizations face mounting pressure to operationalize AI rapidly across diverse environments. MLOps platforms enable streamlined deployment, monitoring, and governance of models at scale. Vendors are embedding orchestration and automation features to reduce manual intervention. Rising demand for efficiency and speed is amplifying adoption across industries such as finance, healthcare, and retail. The push for scalable deployment automation is positioning MLOps platforms as a critical enabler of enterprise AI strategies.

Restraint:

Complex integration with legacy systems

Enterprises encounter difficulties aligning modern workflows with outdated IT infrastructure. Smaller firms face higher challenges compared to incumbents with established modernization budgets. The lack of interoperability across multi-vendor systems adds further delays. Vendors are introducing modular frameworks and APIs to ease integration burdens. Persistent complexity is slowing penetration making compatibility a decisive factor for scaling MLOps platforms.

Opportunity:

Growth in edge AI and IoT deployments

Growth in edge AI and IoT deployments is creating strong opportunities for MLOps providers. Connected device adoption is driving demand for platforms that manage models at the edge. Real-time monitoring and retraining capabilities strengthen responsiveness in dynamic environments. Vendors are embedding lightweight orchestration tools to support distributed deployments. Investment in IoT ecosystems is amplifying demand for scalable MLOps frameworks. The convergence of edge AI and IoT is redefining MLOps as a driver of decentralized intelligence.

Threat:

Data privacy and regulatory challenges

Enterprises face rising scrutiny over AI systems handling sensitive personal and financial data. Smaller providers struggle to maintain compliance compared to incumbents with larger resources. Regulatory frameworks across regions add complexity to deployment strategies. Vendors are embedding encryption and anonymization features to strengthen trust. The growing regulatory burden is reshaping priorities making privacy resilience central to MLOps success.

Covid-19 Impact:

The Covid-19 pandemic accelerated demand for MLOps platforms as enterprises scaled AI to manage crisis-driven workloads. On one hand, supply chain disruptions slowed infrastructure projects and delayed modernization efforts. On the other hand, rising reliance on AI in healthcare, logistics, and retail boosted adoption of MLOps frameworks. Enterprises increasingly relied on automated monitoring and retraining to maintain accuracy during volatile conditions. Vendors embedded explainability and compliance features to strengthen trust. The pandemic underscored MLOps platforms as essential for balancing innovation with accountability in uncertain environments.

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

The software segment is expected to account for the largest market share during the forecast period, driven by demand for platforms that streamline deployment and monitoring. Enterprises are embedding software-based orchestration into AI workflows to strengthen scalability and compliance. Vendors are developing solutions that integrate automation, retraining, and governance features. Rising demand for efficiency in regulated industries is amplifying adoption in this segment. Enterprises view software platforms as critical for sustaining operational resilience and trust. The dominance of software reflects its role as the backbone of MLOps ecosystems.

The model retraining segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the model retraining segment is predicted to witness the highest growth rate, supported by rising demand for adaptive AI systems. Enterprises increasingly require retraining frameworks to ensure models remain accurate with evolving datasets. Vendors are embedding automated retraining pipelines into MLOps platforms to strengthen responsiveness. SMEs and large institutions benefit from scalable retraining tailored to diverse industries. Rising investment in AI-driven automation is amplifying demand in this segment. The growth of model retraining highlights its role in redefining MLOps as a proactive optimization tool.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by mature AI infrastructure and strong enterprise adoption of MLOps platforms. Enterprises in the United States and Canada are leading investments in compliance-driven frameworks to align with regulatory mandates. The presence of major technology providers further strengthens regional dominance. Rising demand for scalable AI deployment is amplifying adoption across industries. Vendors are embedding advanced orchestration 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 innovation initiatives. Countries such as China, India, and Southeast Asia are investing heavily in MLOps platforms to support AI-driven growth. Local enterprises are adopting retraining and orchestration tools to strengthen scalability and meet regulatory expectations. Startups and regional vendors are deploying cost-effective solutions tailored to diverse markets. Government programs promoting digital transformation and AI adoption are accelerating demand. Asia Pacific's trajectory is defined by its ability to scale innovation quickly positioning it as the fastest-growing hub for MLOps platforms worldwide.

Key players in the market

Some of the key players in MLOps Platforms Market include IBM Corporation, Microsoft Corporation, Google Cloud, Amazon Web Services, Inc., Salesforce, Inc., SAP SE, Oracle Corporation, DataRobot, Inc., Fiddler AI, Inc., Arthur AI, Inc., H2O.ai, Inc., Domino Data Lab, Inc., Weights & Biases, Inc., Intel Corporation and Allegro AI, Inc.

Key Developments:

In March 2024, Microsoft expanded its Azure AI infrastructure globally with new NVIDIA H100 Tensor Core GPU-based virtual machines, significantly scaling the high-performance computing backbone required for training and serving large models. This infrastructure expansion directly supported the scalability demands of enterprise MLOps pipelines on Azure.

In May 2023, IBM and SAP expanded their longstanding partnership to integrate SAP software with IBM's hybrid cloud and AI solutions, including Watson AI. This collaboration specifically aims to provide joint customers with industry-specific AI workflows and MLOps capabilities embedded within SAP environments.

Components Covered:

  • Software
  • Services

ML Framework Supports Covered:

  • TensorFlow
  • PyTorch
  • Scikit-learn
  • XGBoost
  • Multi-Framework Support
  • Other ML Framework Supports

Deployment Models Covered:

  • On-Premise
  • Cloud

Lifecycle Stages Covered:

  • Model Development
  • Model Deployment
  • Model Monitoring
  • Model Retraining
  • Model Retirement
  • Other Lifecycle Stages

End Users Covered:

  • BFSI
  • Healthcare
  • Retail & E-Commerce
  • IT & Telecom
  • Manufacturing
  • Energy & Utilities
  • Government
  • 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 MLOps Platforms Market, By Component

  • 5.1 Introduction
  • 5.2 Software
    • 5.2.1 Model Development Platforms
    • 5.2.2 Model Deployment Tools
    • 5.2.3 Monitoring & Drift Detection
    • 5.2.4 Bias & Fairness Tools
  • 5.3 Service
    • 5.3.1 Model Development Platforms
    • 5.3.2 Model Deployment Tools
    • 5.3.3 Monitoring & Drift Detection
    • 5.3.4 Bias & Fairness Tools

6 Global MLOps Platforms Market, By ML Framework Support

  • 6.1 Introduction
  • 6.2 TensorFlow
  • 6.3 PyTorch
  • 6.4 Scikit-learn
  • 6.5 XGBoost
  • 6.6 Multi-Framework Support
  • 6.7 Other ML Framework Supports

7 Global MLOps Platforms Market, By Deployment Model

  • 7.1 Introduction
  • 7.2 On-Premise
  • 7.3 Cloud

8 Global MLOps Platforms Market, By Lifecycle Stage

  • 8.1 Introduction
  • 8.2 Model Development
  • 8.3 Model Deployment
  • 8.4 Model Monitoring
  • 8.5 Model Retraining
  • 8.6 Model Retirement
  • 8.7 Other Lifecycle Stages

9 Global MLOps Platforms Market, By End User

  • 9.1 Introduction
  • 9.2 BFSI
  • 9.3 Healthcare
  • 9.4 Retail & E-Commerce
  • 9.5 IT & Telecom
  • 9.6 Manufacturing
  • 9.7 Energy & Utilities
  • 9.8 Government
  • 9.9 Media & Entertainment
  • 9.10 Other End Users

10 Global MLOps Platforms 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 DataRobot, Inc.
  • 12.9 Fiddler AI, Inc.
  • 12.10 Arthur AI, Inc.
  • 12.11 H2O.ai, Inc.
  • 12.12 Domino Data Lab, Inc.
  • 12.13 Weights & Biases, Inc.
  • 12.14 Intel Corporation
  • 12.15 Allegro AI, Inc.

List of Tables

  • Table 1 Global MLOps Platforms Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global MLOps Platforms Market Outlook, By Component (2024-2032) ($MN)
  • Table 3 Global MLOps Platforms Market Outlook, By Software (2024-2032) ($MN)
  • Table 4 Global MLOps Platforms Market Outlook, By Model Development Platforms (2024-2032) ($MN)
  • Table 5 Global MLOps Platforms Market Outlook, By Model Deployment Tools (2024-2032) ($MN)
  • Table 6 Global MLOps Platforms Market Outlook, By Monitoring & Drift Detection (2024-2032) ($MN)
  • Table 7 Global MLOps Platforms Market Outlook, By Bias & Fairness Tools (2024-2032) ($MN)
  • Table 8 Global MLOps Platforms Market Outlook, By Service (2024-2032) ($MN)
  • Table 9 Global MLOps Platforms Market Outlook, By ML Framework Support (2024-2032) ($MN)
  • Table 10 Global MLOps Platforms Market Outlook, By TensorFlow (2024-2032) ($MN)
  • Table 11 Global MLOps Platforms Market Outlook, By PyTorch (2024-2032) ($MN)
  • Table 12 Global MLOps Platforms Market Outlook, By Scikit-learn (2024-2032) ($MN)
  • Table 13 Global MLOps Platforms Market Outlook, By XGBoost (2024-2032) ($MN)
  • Table 14 Global MLOps Platforms Market Outlook, By Multi-Framework Support (2024-2032) ($MN)
  • Table 15 Global MLOps Platforms Market Outlook, By Other ML Framework Support (2024-2032) ($MN)
  • Table 16 Global MLOps Platforms Market Outlook, By Deployment Model (2024-2032) ($MN)
  • Table 17 Global MLOps Platforms Market Outlook, By On-Premise (2024-2032) ($MN)
  • Table 18 Global MLOps Platforms Market Outlook, By Cloud (2024-2032) ($MN)
  • Table 19 Global MLOps Platforms Market Outlook, By Lifecycle Stage (2024-2032) ($MN)
  • Table 20 Global MLOps Platforms Market Outlook, By Model Development (2024-2032) ($MN)
  • Table 21 Global MLOps Platforms Market Outlook, By Model Deployment (2024-2032) ($MN)
  • Table 22 Global MLOps Platforms Market Outlook, By Model Monitoring (2024-2032) ($MN)
  • Table 23 Global MLOps Platforms Market Outlook, By Model Retraining (2024-2032) ($MN)
  • Table 24 Global MLOps Platforms Market Outlook, By Model Retirement (2024-2032) ($MN)
  • Table 25 Global MLOps Platforms Market Outlook, By Other Lifecycle Stages (2024-2032) ($MN)
  • Table 26 Global MLOps Platforms Market Outlook, By End User (2024-2032) ($MN)
  • Table 27 Global MLOps Platforms Market Outlook, By BFSI (2024-2032) ($MN)
  • Table 28 Global MLOps Platforms Market Outlook, By Healthcare (2024-2032) ($MN)
  • Table 29 Global MLOps Platforms Market Outlook, By Retail & E-Commerce (2024-2032) ($MN)
  • Table 30 Global MLOps Platforms Market Outlook, By IT & Telecom (2024-2032) ($MN)
  • Table 31 Global MLOps Platforms Market Outlook, By Manufacturing (2024-2032) ($MN)
  • Table 32 Global MLOps Platforms Market Outlook, By Energy & Utilities (2024-2032) ($MN)
  • Table 33 Global MLOps Platforms Market Outlook, By Government (2024-2032) ($MN)
  • Table 34 Global MLOps Platforms Market Outlook, By Media & Entertainment (2024-2032) ($MN)
  • Table 35 Global MLOps Platforms 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.