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PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1664839

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PUBLISHER: Global Market Insights Inc. | PRODUCT CODE: 1664839

MLOps Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034

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PAGES: 180 Pages
DELIVERY TIME: 2-3 business days
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The Global MLOps Market was valued at USD 1.7 billion in 2024 and is forecasted to grow at a robust CAGR of 37.4% from 2025 to 2034. The increasing shift towards cloud computing serves as a major driver, as cloud platforms offer the scalability and flexibility needed to manage extensive datasets and complex machine learning workflows efficiently.

MLOps Market - IMG1

Cloud-based MLOps solutions enable organizations to deploy models seamlessly across multiple environments. This approach eliminates the need for extensive on-premises infrastructure while delivering enhanced performance and scalability. By leveraging these solutions, businesses can streamline machine learning operations and adapt to evolving demands with greater efficiency.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$1.7 billion
Forecast Value$39 billion
CAGR37.4%

Reducing the time-to-market for new machine learning models has become a critical priority for organizations aiming to maintain a competitive edge. MLOps platforms facilitate this by automating the development, testing, and deployment processes through continuous integration and continuous deployment (CI/CD). This automation accelerates workflows, minimizes manual intervention, and ensures models remain scalable and consistently updated.

The MLOps market is segmented by components into platforms and services. Platforms led the market in 2024, capturing 72% of the total share. This dominance stems from the growing demand for end-to-end solutions that unify data pipeline management, model deployment, experiment tracking, and performance monitoring. Comprehensive platforms are increasingly favored by enterprises seeking to scale artificial intelligence initiatives while simplifying their workflows.

Services, including consulting, integration, and managed services, are also witnessing significant growth. These services assist organizations in overcoming adoption challenges such as cloud migration, infrastructure optimization, and compliance requirements. The rise in demand for tailored guidance highlights the importance of expert support in the MLOps ecosystem.

By end use, the market is categorized into Large Enterprises and SME. In 2024, Large Enterprises held a 64.3% market share, driven by the adoption of MLOps solutions to optimize AI workflows, enhance predictive analytics, and improve governance. Meanwhile, SME are rapidly embracing cost-effective, user-friendly tools that enable them to automate processes and foster innovation. The growing accessibility of AI tools supports this trend, allowing smaller businesses to achieve scalability without heavy infrastructure investments.

In North America, the United States leads the MLOps market, projected to surpass USD 11 billion by 2034. The country's strong adoption of AI and machine learning across industries such as healthcare, finance, and manufacturing underscores its pivotal role in driving market expansion. Investments in cloud infrastructure and high-performance computing further propel the adoption of MLOps solutions, helping businesses improve model operations and reduce deployment times.

Product Code: 12478

Table of Contents

Chapter 1 Methodology & Scope

  • 1.1 Research design
    • 1.1.1 Research approach
    • 1.1.2 Data collection methods
  • 1.2 Base estimates and calculations
    • 1.2.1 Base year calculation
    • 1.2.2 Key trends for market estimates
  • 1.3 Forecast model
  • 1.4 Primary research & validation
    • 1.4.1 Primary sources
    • 1.4.2 Data mining sources
  • 1.5 Market definitions

Chapter 2 Executive Summary

  • 2.1 Industry 3600 synopsis, 2021 - 2034

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Technology providers
    • 3.1.2 Model development and training platforms
    • 3.1.3 Data management providers
    • 3.1.4 Model deployment and governance providers
    • 3.1.5 End users
  • 3.2 Supplier landscape
  • 3.3 Profit margin analysis
  • 3.4 Use cases of MLOps
  • 3.5 Technology & innovation landscape
  • 3.6 Key news & initiatives
  • 3.7 Regulatory landscape
  • 3.8 Impact forces
    • 3.8.1 Growth drivers
      • 3.8.1.1 Increased adoption of AI and machine learning
      • 3.8.1.2 Demand for faster model deployment
      • 3.8.1.3 Regulatory compliance and model governance
      • 3.8.1.4 Cloud adoption and scalability
    • 3.8.2 Industry pitfalls & challenges
      • 3.8.2.1 Data privacy and security concerns
      • 3.8.2.2 Lack of skilled professionals
  • 3.9 Growth potential analysis
  • 3.10 Porter’s analysis
  • 3.11 PESTEL analysis

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
  • 4.3 Competitive positioning matrix
  • 4.4 Strategic outlook matrix

Chapter 5 Market Estimates & Forecast, By Component, 2021 - 2034 ($Mn)

  • 5.1 Key trends
  • 5.2 Platform
  • 5.3 Services

Chapter 6 Market Estimates & Forecast, By Deployment Mode, 2021 - 2034 ($Mn)

  • 6.1 Key trends
  • 6.2 Cloud-based
  • 6.3 On-Premises

Chapter 7 Market Estimates & Forecast, By End Use, 2021-2034 ($Mn)

  • 7.1 Key trends
  • 7.2 Large enterprises
  • 7.3 SME

Chapter 8 Market Estimates & Forecast, By Vertical, 2021 - 2034 ($Mn)

  • 8.1 Key trends
  • 8.2 Healthcare
  • 8.3 Retail & e-commerce
  • 8.4 Manufacturing & supply chain
  • 8.5 BFSI
  • 8.6 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021 - 2034 ($Mn)

  • 9.1 Key trends
  • 9.2 North America
    • 9.2.1 U.S.
    • 9.2.2 Canada
  • 9.3 Europe
    • 9.3.1 UK
    • 9.3.2 Germany
    • 9.3.3 France
    • 9.3.4 Spain
    • 9.3.5 Italy
    • 9.3.6 Russia
    • 9.3.7 Nordics
  • 9.4 Asia Pacific
    • 9.4.1 China
    • 9.4.2 India
    • 9.4.3 Japan
    • 9.4.4 South Korea
    • 9.4.5 ANZ
    • 9.4.6 Southeast Asia
  • 9.5 Latin America
    • 9.5.1 Brazil
    • 9.5.2 Mexico
    • 9.5.3 Argentina
  • 9.6 MEA
    • 9.6.1 UAE
    • 9.6.2 South Africa
    • 9.6.3 Saudi Arabia

Chapter 10 Company Profiles

  • 10.1 Alteryx
  • 10.2 Amazon Web Services (AWS)
  • 10.3 Atos
  • 10.4 Capgemini
  • 10.5 Cisco
  • 10.6 Cloudera
  • 10.7 Databricks
  • 10.8 Google Cloud
  • 10.9 H2O.ai
  • 10.10 IBM
  • 10.11 Microsoft
  • 10.12 NVIDIA
  • 10.13 Oracle
  • 10.14 Red Hat
  • 10.15 Salesforce
  • 10.16 SAP
  • 10.17 Siemens
  • 10.18 TIBCO Software
  • 10.19 VMware
  • 10.20 Weights & Biases
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Jeroen Van Heghe

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Christine Sirois

Manager - Americas

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