Luton, Bedfordshire, United Kingdom, June 13, 2025 (GLOBE NEWSWIRE) — Market Overview and Growth Forecast
The global Machine Learning Operations (MLOps) market is witnessing significant growth, driven by the accelerating adoption of artificial intelligence (AI) and machine learning (ML) technologies across multiple industries. Valued at approximately $4.5 billion in 2024, the market is projected to reach around $20 billion by 2034, expanding at a Compound Annual Growth Rate (CAGR) of 16.5%. This upward trajectory reflects the increasing demand for solutions that streamline the deployment, monitoring, and management of machine learning models.
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MLOps plays a pivotal role in bridging the gap between data science and IT operations by enabling seamless collaboration, version control, and model lifecycle automation. The integration of MLOps into business operations facilitates faster time-to-market, improved decision-making, and enhanced model governance, thereby making it an essential component of modern AI infrastructure.
Deployment Type Analysis: Cloud-Based vs. On-Premises
MLOps solutions are typically deployed via cloud-based or on-premises models. Cloud-based deployments currently dominate the market, accounting for over 70% of the total share, owing to their cost-effectiveness, scalability, and ease of integration with existing cloud ecosystems. Enterprises favor these solutions for their ability to handle large-scale ML deployments, offer collaborative environments, and minimize infrastructure costs.
On the other hand, on-premises solutions retain a niche but crucial role in sectors such as finance and healthcare, where stringent data security, regulatory compliance, and legacy system integration are paramount. Although on-premises solutions represent a smaller share—approximately 30%—they are indispensable for organizations with critical data governance requirements.
Component Breakdown: Solutions and Services
The MLOps market is segmented into solutions and services. Solutions—which include tools for automation, model tracking, version control, and monitoring—constitute the majority of the market, accounting for over 65%. As enterprises look to operationalize ML workflows and ensure compliance with internal and external regulations, the demand for robust solutions continues to grow.
Meanwhile, services such as consulting, support, and training represent the remaining 35%. These services are essential for organizations at the initial stages of MLOps adoption or undergoing digital transformation. Service providers help businesses align their ML strategy with operational goals, ensuring successful integration and scaling of MLOps platforms.
Application Areas Driving Market Adoption
MLOps is being increasingly applied across a diverse range of use cases, with predictive maintenance emerging as a leading segment. Used primarily in manufacturing and industrial sectors, predictive maintenance leverages ML models to anticipate equipment failures, thereby reducing downtime and optimizing maintenance schedules. This segment is expected to command around 30% of the market share.
Fraud detection, particularly in the financial sector, is another vital application of MLOps, capturing approximately 25% of the market. Real-time data processing and anomaly detection models have become indispensable in combating evolving fraud tactics.
Customer experience management is also gaining traction, with about 20% of the share. Businesses are employing ML-driven personalization and customer analytics to improve engagement and satisfaction. Other applications such as marketing analytics and supply chain optimization collectively account for the remaining 25%, showcasing the broad utility of MLOps across business functions.
Industry Vertical Insights
From a vertical standpoint, IT and telecommunications lead the adoption of MLOps, representing roughly 25% of total market revenue. These sectors rely on rapid innovation cycles and scalable infrastructure to deploy AI models effectively. Healthcare follows closely, accounting for around 20%, driven by the need for predictive diagnostics, patient data management, and improved operational efficiency.
The retail sector, with a 15% share, leverages MLOps for demand forecasting, inventory management, and personalization. Other notable contributors include manufacturing and financial services, each holding 10–15%, while the government and media sectors are gradually expanding their usage of MLOps for intelligent automation and data governance.
Adoption by Organization Size
Large enterprises dominate the MLOps landscape, accounting for nearly 60% of market revenues. These organizations have the capital and resources necessary to invest in end-to-end ML infrastructures and tailor solutions for complex use cases.
Small and medium-sized enterprises (SMEs), however, are a fast-growing segment, holding the remaining 40%. The increasing availability of affordable, scalable, and cloud-native MLOps platforms is helping SMEs embrace AI technologies without the need for massive upfront investments. As awareness and education around AI-driven growth increase, SME adoption is expected to rise substantially in the coming decade.
Technology and Distribution Trends
MLOps solutions are underpinned by technologies like artificial intelligence, big data analytics, and DevOps practices. The convergence of AI with DevOps has led to the creation of automated pipelines, reducing the friction between development and operations. The synergy among these technologies is crucial to enabling continuous integration and delivery of machine learning applications.
In terms of distribution, direct sales remain dominant, particularly for enterprise clients that require customized solutions and service-level agreements. However, online sales channels are gaining traction, especially among SMEs and startups seeking rapid, on-demand access to tools and services.
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Market Segmentation
By Deployment Type
– Cloud-based
– On-premises
By Component
– Solutions
– Services
By Application
– Predictive Maintenance
– …