Dublin, March 14, 2025 (GLOBE NEWSWIRE) -- The "ModelOps - Global Strategic Business Report" report has been added to ResearchAndMarkets.com's offering.

The global market for ModelOps was valued at US$5.8 Billion in 2024 and is projected to reach US$43.2 Billion by 2030, growing at a CAGR of 39.6% from 2024 to 2030. This comprehensive report provides an in-depth analysis of market trends, drivers, and forecasts, helping you make informed business decisions.



The growth in the ModelOps market is driven by several factors, including technological advancements, evolving enterprise needs, and changes in operational practices. The increasing complexity of AI model lifecycles has heightened the demand for robust ModelOps solutions that can handle version control, governance, and compliance with industry standards. Rising adoption of cloud-native technologies and multi-cloud environments has spurred the need for scalable ModelOps platforms that offer seamless integration.

Enterprises are also seeking solutions to automate model monitoring and retraining, reducing operational inefficiencies and ensuring optimal performance. Regulatory pressures related to AI ethics, fairness, and accountability have further accelerated the adoption of ModelOps frameworks. Additionally, the growing emphasis on operationalizing AI for business value, coupled with the rising deployment of real-time AI systems in mission-critical applications, is significantly driving market growth.

What is ModelOps, and Why is It Transforming Enterprise AI Integration?

ModelOps, short for Model Operations, is a specialized framework designed to manage, deploy, and monitor artificial intelligence (AI) and machine learning (ML) models in production environments. It bridges the gap between data science and IT operations, ensuring that AI models transition seamlessly from development to deployment while maintaining optimal performance.

Unlike traditional DevOps, which focuses on software lifecycle management, ModelOps emphasizes the lifecycle of AI models, including versioning, governance, and compliance. This approach is gaining traction as enterprises increasingly rely on AI to enhance decision-making, automate processes, and gain competitive advantages. The rapid adoption of digital transformation initiatives across industries has made ModelOps an essential tool in ensuring the scalability, efficiency, and accountability of AI systems.

How is Technology Driving Innovations in ModelOps?

The ModelOps ecosystem is evolving rapidly due to advancements in underlying technologies such as automation, cloud computing, and containerization. Tools powered by AI and machine learning are now integrated into ModelOps frameworks to facilitate automated model monitoring, performance optimization, and anomaly detection.

Cloud-based ModelOps platforms have democratized access to AI operations by offering scalability and flexibility to businesses of all sizes. Additionally, the rise of multi-cloud and hybrid cloud environments has introduced new complexities, prompting innovations in platform-agnostic ModelOps solutions. Real-time data analytics and the integration of robust security protocols have further enhanced the utility and reliability of these systems, addressing critical concerns such as data privacy and model bias.

Which Industries are Adopting ModelOps, and How are They Benefiting?

The adoption of ModelOps spans diverse industries, with each leveraging the framework to address unique challenges and opportunities. In the financial services sector, ModelOps is used to manage risk models, detect fraud, and enhance customer personalization through predictive analytics. Healthcare organizations rely on ModelOps to streamline diagnostics, improve patient care, and accelerate drug discovery processes. In manufacturing, it facilitates predictive maintenance and supply chain optimization.

Retailers use ModelOps to enhance inventory management and improve marketing efficiency through customer segmentation models. The public sector is also adopting ModelOps to improve service delivery and decision-making in areas like urban planning and emergency response. The growing reliance on AI-driven solutions across all these sectors underscores the importance of ModelOps in ensuring model transparency, accuracy, and performance.

Report Features:

• Comprehensive Market Data: Independent analysis of annual sales and market forecasts in US$ Million from 2024 to 2030.
• In-Depth Regional Analysis: Detailed insights into key markets, including the U.S., China, Japan, Canada, Europe, Asia-Pacific, Latin America, Middle East, and Africa.
• Company Profiles: Coverage of players such as appengine.ai Inc, C3.ai, Inc., Databricks, Inc., DataKitchen, Datatron and more.
• Complimentary Updates: Receive free report updates for one year to keep you informed of the latest market developments.

Report Scope

• Offering (ModelOps Platform, ModelOps Services)
• Model Type (Machine Learning Models, Graph-based Models, Rule & Heuristic Models, Linguistic Models, Agent-based Models, Bring Your Own Models, Other Model Types)
• Application (Continuous Integration / Continuous Deployment Application, Monitoring & Alerting Application, Dashboard & Reporting Application, Model Lifecycle Management Application, Governance, Risk & Compliance Application, Parallelization & Distributed Computing Application, Batch Scoring Application, Other Applications)
• Vertical (BFSI Vertical, Retail & E-Commerce Vertical, Healthcare & Life Sciences Vertical, Telecommunications Vertical, Government & Defense Vertical, IT / ITeS Vertical, Energy & Utilities Vertical, Other Verticals)

Key Attributes:

Key Topics Covered:

MARKET OVERVIEW

• Influencer Market Insights
• World Market Trajectories
• Economic Frontiers: Trends, Trials & Transformations
• ModelOps - Global Key Competitors Percentage Market Share in 2025 (E)
• Competitive Market Presence - Strong/Active/Niche/Trivial for Players Worldwide in 2025 (E)

MARKET TRENDS & DRIVERS

• Accelerating Demand for Automated ML Drives Market Growth
• Cloud-Native ModelOps Platforms Propel Scalability and Adoption
• Investments in Explainable AI Boost Trust and Market Confidence
• Industry-Specific AI Applications Expand Opportunities Across Sectors
• Focus on Model Governance Fuels Demand for Compliance Solutions
• Continuous Monitoring of AI Models Drives Operational Efficiency
• ModelOps Integration With DevOps Enhances Scalability and Lifecycles
• Pre-Built AI Solutions Create Opportunities for Faster Deployments
• Advancements in Automation Tools Stimulate Broader Adoption of ModelOps
• AI Democratization Expands Usability Across Non-Technical Teams
• Predictive Analytics Adoption Drives Growth Across Industries
• Generative AI Usage Propels Innovation and Customization in ModelOps Solutions

FOCUS ON SELECT PLAYERS: Some of the 41 companies featured in this ModelOps market report include:

• appengine.ai Inc
• C3.ai, Inc.
• Databricks, Inc.
• DataKitchen
• Datatron
• Evidently AI, Inc
• Giggso
• IBM Corporation
• iFusion Inc
• Minitab LLC

For more information about this report visit https://www.researchandmarkets.com/r/erw85e

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• ModelOps Market