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1916693

全球機器學習維運平台市場：預測至 2032 年 - 按組件、機器學習框架支援、部署方法、生命週期階段、最終用戶和地區進行分析

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個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球 MLOps 平台市場價值將達到 18.5 億美元，到 2032 年將達到 214.9 億美元，在預測期內的複合年成長率將達到 42%。

MLOps平台是整合式軟體解決方案，使組織能夠以可擴展、管治和可控的方式管理機器學習模型的端到端生命週期。它們在一個統一的框架內整合了資料準備、模型開發、訓練、測試、部署、監控和重新訓練等工具。 MLOps平台支援資料科學家、工程師和IT團隊之間的協作，同時確保版本控制、可重複性、安全性和合規性。透過工作流程自動化和對模型效能及偏差的持續監控，這些平台幫助企業有效率地部署機器學習，縮短生產週期，並在各種環境中維護可靠、高品質的AI系統。

可擴展模型部署自動化的需求

企業面臨越來越大的壓力，需要在各種環境中快速部署人工智慧。 MLOps 平台能夠簡化大規模模式部署、監控和管治。供應商正在整合編配和自動化功能，以減少人工干預。對效率和速度日益成長的需求正在加速金融、醫療保健和零售等行業的採用。對可擴展部署自動化的需求使 MLOps 平台成為企業人工智慧策略的關鍵驅動力。

與舊有系統的複雜整合

企業在將現代化工作流程與過時的IT基礎設施相容方面面臨許多挑戰。與擁有成熟現代化預算的大型企業相比，中小企業面臨的挑戰更大。多供應商系統之間缺乏互通性，進一步加劇了延誤。供應商正在引入模組化框架和API以減輕整合負擔。持續的複雜性減緩了採用速度，因此相容性成為MLOps平台擴展的關鍵因素。

邊緣人工智慧和物聯網的應用日益廣泛

邊緣人工智慧和物聯網的日益普及為機器學習運維（MLOps）供應商創造了巨大的成長機會。互聯設備的激增推動了對邊緣環境模型管理平台的需求。即時監控和重新訓練功能增強了模型在動態環境中的反應能力。供應商正在整合輕量級編配工具以支援分散式部署。對物聯網生態系統的投資正在推動可擴展MLOps框架的需求。邊緣人工智慧和物聯網的融合正在重新定義MLOps，使其成為分散式智慧的促進者。

資料隱私和監管挑戰

企業在處理敏感個人和財務數據的AI系統方面面臨日益嚴格的審查。與擁有更雄厚資源的現有企業相比，小規模的供應商更難維持合規性。區域法規結構也增加了部署策略的複雜性。供應商正在整合加密和匿名化功能以增強信任。日益成長的監管負擔正在重塑優先事項，使隱私保護成為MLOps成功的核心要素。

新冠疫情的影響：

新冠疫情加速了對機器學習運維（MLOps）平台的需求，因為企業正在擴展人工智慧（AI）應用以管理危機應變工作。同時，供應鏈中斷導致基礎設施計劃延期，現代化進程受阻。此外，醫療保健、物流和零售業對人工智慧的日益依賴推動了MLOps框架的普及。企業越來越依賴自動化監控和再訓練來確保在動盪環境下的準確性。供應商則在平台中加入了可解釋性和合規性功能，以增強可靠性。疫情凸顯了MLOps平台在不確定環境中平衡創新與課責的迫切需求。

預計在預測期內，軟體領域將佔據最大的市場佔有率。

在預測期內，軟體領域預計將佔據最大的市場佔有率，這主要得益於對能夠簡化部署和監控的平台的需求。企業正在將基於軟體的編配整合到人工智慧工作流程中，以提高可擴展性和合規性。供應商正在開發整合自動化、再培訓和管治功能的解決方案。受監管行業對效率日益成長的需求正在加速該領域的應用。企業將軟體平台視為維持營運彈性和可靠性的關鍵。軟體的主導地位反映了其作為MLOps生態系統基礎的角色。

在預測期內，模型重訓練部分將呈現最高的複合年成長率。

受自適應人工智慧系統需求不斷成長的推動，模型重訓練領域預計將在預測期內實現最高成長率。企業越來越需要重訓練框架來適應不斷變化的資料集並保持模型精確度。為了提高應對力，供應商正在將自動化重訓練流程整合到其MLOps平台中。從中小企業到大型企業，各行各業都能受益於可擴展的、針對不同行業量身定做的重訓練方案。對人工智慧驅動的自動化領域不斷成長的投資正在推動該領域的需求。模型重訓練的成長凸顯了將MLOps重新定義為一種主動最佳化工具的重要性。

佔比最大的地區：

由於成熟的人工智慧基礎設施和MLOps平台在企業中的廣泛應用，預計北美將在預測期內保持最大的市場佔有率。美國和加拿大的公司在投資主導規框架以滿足監管要求方面主導。主要技術提供商的存在進一步鞏固了該地區的領先地位。可擴展人工智慧部署的需求不斷成長，正在推動各行業的應用。供應商正在整合先進的編配和監控功能，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、人工智慧的日益普及以及政府主導的創新舉措。中國、印度和東南亞等國家正大力投資機器學習維運（MLOps）平台，以支援其人工智慧主導的成長。當地企業正在採用重新訓練和編配工具來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成本效益解決方案。政府推動數位轉型和人工智慧應用的計畫正在加速市場需求。亞太地區的成長軌跡以其快速擴展創新規模的能力為特徵，使其成為全球成長最快的機器學習維運平台中心。

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  • 區域分類
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    • 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MLOps 平台市場（按組件分類）

  • 軟體
    • 模型開發平台
    • 模型部署工具
    • 監測和漂移檢測
    • 偏見與公平工具
  • 服務
    • 模型開發平台
    • 模型部署工具
    • 監測和漂移檢測
    • 偏見與公平工具

6. 全球MLOps平台市場（以機器學習架構支援分類）

  • TensorFlow
  • PyTorch
  • Scikit-learn
  • XGBoost
  • 支援多種框架
  • 其他機器學習框架支持

7. 全球MLOps平台市場依部署方式分類

  • 本地部署
  • 雲

8. 全球MLOps平台市場依生命週期階段分類

  • 模型開發
  • 模型部署
  • 模型監測
  • 模型重新訓練
  • 模範退休
  • 其他生命週期階段

9.全球MLOps 平台市場（依最終用戶分類）

  • BFSI
  • 醫療保健
  • 零售與電子商務
  • 資訊科技/通訊
  • 製造業
  • 能源與公用事業
  • 政府
  • 媒體與娛樂
  • 其他最終用戶

第10章：全球MLOps平台市場（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
    • 其他南美洲
  • 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
  • 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.