The Global ModelOps Market size is expected to reach $58.07 billion by 2031, rising at a market growth of 40.2% CAGR during the forecast period.
The North America segment garnered 36% revenue share in the market in 2023. This prominence is attributed to the region's advanced technological infrastructure, substantial investments in artificial intelligence (AI) and machine learning (ML), and the presence of numerous leading technology firms. Industries such as finance, healthcare, and retail in North America are increasingly adopting these solutions to streamline AI model deployment and management, ensuring compliance with stringent regulatory standards and enhancing operational efficiency.
Companies leverage AI and ML to optimize operations, streamline workflows, and uncover insights that drive smarter decision-making. For example, in the financial sector, artificial intelligence models are employed for the purposes of fraud detection and risk assessment. Conversely, in the retail industry, these models facilitate personalized recommendations and optimize inventory management. Hence, this combination of technological capability and operational efficiency is driving the rapid adoption of ModelOps.
Additionally, Operational efficiency has become a top priority for organizations striving to remain agile and competitive in today’s fast-paced business environment. A significant challenge in attaining this efficacy resides in the management of the complexities associated with the deployment and maintenance of artificial intelligence and machine learning models within production environments. Thus, these developments aid in the expansion of the market.
However, The dearth of qualified data scientists and machine learning engineers means that organizations often lack the necessary expertise to develop robust machine learning models. This limitation leads to longer development cycles, as existing teams may be overburdened or lack specific skills required for certain projects. Moreover, without adequate ModelOps experts, deploying these models into production environments becomes challenging. Hence, this substantial lack of talent may hamper the expansion of the market.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Acquisitions, and Partnerships & Collaborations.
The North America segment garnered 36% revenue share in the market in 2023. This prominence is attributed to the region's advanced technological infrastructure, substantial investments in artificial intelligence (AI) and machine learning (ML), and the presence of numerous leading technology firms. Industries such as finance, healthcare, and retail in North America are increasingly adopting these solutions to streamline AI model deployment and management, ensuring compliance with stringent regulatory standards and enhancing operational efficiency.
Companies leverage AI and ML to optimize operations, streamline workflows, and uncover insights that drive smarter decision-making. For example, in the financial sector, artificial intelligence models are employed for the purposes of fraud detection and risk assessment. Conversely, in the retail industry, these models facilitate personalized recommendations and optimize inventory management. Hence, this combination of technological capability and operational efficiency is driving the rapid adoption of ModelOps.
Additionally, Operational efficiency has become a top priority for organizations striving to remain agile and competitive in today’s fast-paced business environment. A significant challenge in attaining this efficacy resides in the management of the complexities associated with the deployment and maintenance of artificial intelligence and machine learning models within production environments. Thus, these developments aid in the expansion of the market.
However, The dearth of qualified data scientists and machine learning engineers means that organizations often lack the necessary expertise to develop robust machine learning models. This limitation leads to longer development cycles, as existing teams may be overburdened or lack specific skills required for certain projects. Moreover, without adequate ModelOps experts, deploying these models into production environments becomes challenging. Hence, this substantial lack of talent may hamper the expansion of the market.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Acquisitions, and Partnerships & Collaborations.
Driving and Restraining Factors
Drivers
- Increased Adoption Of AI And Machine Learning
- Growing Focus On Operational Efficiency
- Exponential Growth Of Data
Restraints
- Lack Of Skilled Professionals
- Integration Challenges With Existing IT Infrastructure
Opportunities
- Growing Importance Of Governance And Compliance
- Advancements In Automation And AI Lifecycle Management
Challenges
- High Initial Investment Costs
- Data Security And Compliance Issues
Offering Outlook
Based on offering, the market is bifurcated into platforms and services. The services segment procured 34% revenue share in the market in 2023. The increasing complexity of AI models necessitates specialized services to manage and optimize these models effectively. Organizations focus on data-driven decision-making, which requires customized solutions tailored to specific business needs.Model Outlook
By model, the market is divided into ML models, graph-based models, rule & heuristic models, linguistic models, agent-based models, and others. The graph-based models segment garnered 16% revenue share in the market in 2023. Social media, telecommunications, and cybersecurity use graph-based models to detect patterns, identify anomalies, and understand intricate network structures. The rising need for advanced analytics to navigate complex data relationships drives the demand for these solutions tailored to efficiently manage and operationalize graph-based models.Deployment Outlook
On the basis of deployment, the market is classified into cloud and on-premise. The on-premise segment recorded 38% revenue share in the market in 2023. Industries such as finance, healthcare, and government, which handle sensitive and confidential information, often prefer on-premises ModelOps solutions to ensure adherence to stringent regulatory requirements and mitigate data breach risks.Vertical Outlook
On the basis of vertical, the market is classified into BFSI, retail & e-commerce, healthcare & life sciences, manufacturing, IT & telecommunications, energy & utilities, transportation & logistics, and others. The healthcare & life sciences segment witnessed 15% revenue share in the market in 2023. The sector is increasingly adopting ModelOps to advance personalized medicine, improve diagnostic accuracy, and streamline administrative processes. The integration of AI models facilitates the analysis of complex medical data, leading to better patient outcomes and optimized treatment plans.
Application Outlook
Based on application, the market is segmented into continuous integration/continuous deployment, batch scoring, governance, risk & compliance, parallelization & distributed computing, monitoring & alerting, dashboard & reporting, model lifecycle management, and others. The batch scoring segment recorded 15% revenue share in the market in 2023. Batch scoring involves processing large volumes of data to generate predictions or insights at scheduled intervals. Industries such as finance and retail utilize batch scoring to analyze historical data for risk assessment, customer segmentation, and inventory management.Regional Outlook
Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The Europe segment procured 31% revenue share in the market in 2023. This market in Europe is significantly influenced by the region's robust commitment to data privacy and regulatory compliance, as evidenced by frameworks such as the General Data Protection Regulation (GDPR). Businesses in a variety of industries, such as manufacturing, healthcare, and finance, are investing in these solutions to ensure that their AI models adhere to these strict guidelines.Recent Strategies Deployed in the Market
- Jan-2025: IBM and e& have formed a strategic partnership to launch an end-to-end AI governance platform. This solution utilizes IBM's watsonx.governance platform, aiming to strengthen e&'s AI governance framework through automated risk management, compliance monitoring, and real-time performance analysis. IBM Consulting will assist e& in implementing the framework, accelerating its development with persona mapping, market research, and architecture patterns.
- Oct-2024: AWS and Box, a leading cloud-based Intelligent Content Management (ICM) platform, have expanded their strategic partnership to bring advanced generative AI models to enterprise content. Box now integrates Amazon Bedrock, offering foundation models like Anthropic's Claude and Amazon Titan for custom AI applications. Customers can leverage their data in Box’s Intelligent Content Cloud for secure, scalable AI use cases, unlocking insights, content generation, and workflow automation across industries. The integration with Amazon Q Business further empowers businesses to apply generative AI while maintaining security and privacy.
- Jul-2024: DataRobot, Inc. partnered with Teradata, a leading provider of data and analytics solutions, to integrate its AI Platform with Teradata VantageCloud and ClearScape Analytics. This integration enables enterprises to scale DataRobot’s AI models within VantageCloud, offering enhanced flexibility, accountability, and security in deploying models. By leveraging ClearScape Analytics’ BYOM capability, users can now operationalize AI models at scale while optimizing costs, empowering businesses to accelerate their AI journey and unlock the full potential of their data.
- Dec-2023: Google LLC launched its Gemini AI model, introducing multimodal capabilities to enhance its services across text, images, and audio. Gemini outperforms GPT-4 in most benchmarks, especially in coding, and will power products like Google Bard. Available via Google Cloud’s Vertex AI for enterprise customers, Gemini promises improved efficiency, security, and scalability, marking a significant leap in Google’s AI efforts.
- Jul-2023: Microsoft and Meta expanded their partnership by supporting the Llama 2 family of large language models (LLMs) on Azure and Windows. This collaboration enables developers to fine-tune and deploy Llama 2 models at scale on Azure, benefiting from powerful tools for training, fine-tuning, and inference. Windows developers also gain the ability to optimize Llama 2 locally using the DirectML execution provider. The collaboration underscores Microsoft’s commitment to offering a robust AI ecosystem and ensuring safety and performance in generative AI development.
List of Key Companies Profiled
- Google LLC (Alphabet Inc.)
- Hewlett Packard Enterprise Company
- IBM Corporation
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- H2O.ai, Inc.
- Cloudera, Inc.
- SAS Institute Inc.
- DataRobot, Inc.
- Domino Data Lab, Inc.
Market Report Segmentation
By Offering
- Platforms
- Services
By Model
- ML Models
- Graph-Based Models
- Rule & Heuristic Models
- Linguistic Models
- Agent-Based Models & Others
By Deployment
- Cloud
- On-Premise
By Vertical
- BFSI
- Retail & E-Commerce
- Healthcare & Life Sciences
- Manufacturing
- IT & Telecommunications
- Transportation & Logistics
- Energy, Utilities & Others
By Application
- Continuous Integration/ Continuous Deployment
- Model Lifecycle Management
- Batch Scoring
- Governance, Risk & Compliance
- Monitoring & Alerting
- Parallelization & Distributed Computing
- Dashboard & Reporting
- Other Application
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Australia
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. Global ModelOps Market by Offering
Chapter 6. Global ModelOps Market by Model
Chapter 7. Global ModelOps Market by Deployment
Chapter 8. Global ModelOps Market by Vertical
Chapter 9. Global ModelOps Market by Application
Chapter 10. Global ModelOps Market by Region
Chapter 11. Company Profiles
Companies Mentioned
- Google LLC (Alphabet Inc.)
- Hewlett Packard Enterprise Company
- IBM Corporation
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- H2O.ai, Inc.
- Cloudera, Inc.
- SAS Institute Inc.
- DataRobot, Inc.
- Domino Data Lab, Inc.
Methodology
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