ModelOps 시장 예측( -2030년) : 제공별, 전개 방식별, 기업 규모별, 기술별, 용도별, 최종사용자별, 지역별 세계 분석

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