Get a Comprehensive Overview of the MLOps Market Report Prepared by P&S Intelligence, Segmented by Component (Platform, Services), Application (Data Management, Modeling), Deployment Mode (On-Premise, Cloud, Hybrid), End User (Data Scientists, ML Engineers, AI Enthusiasts, Data Engineers), Industry (BFSI, Manufacturing, IT and Telecom, Smart Mobility, Retail and E-Commerce, Energy and Utility, Healthcare, Media and Entertainment), and Geographic Regions. This Report Provides Insights From 2019 to 2032.
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Machine Learning Operations Market Analysis
The machine learning operations (MLOps) market size was USD 3.4 billion in 2024, and the market size is predicted to reach USD 29.4 billion by 2032, advancing at a CAGR of 31.1% during 2025–2032.
With the growing trend of imbibing the entities with the advanced technology such as AI, the requirement for ML models and operationalization of machine learning has been deploying rapidly to process automation efficiently. It is a process of deploying statistical tools and methods on the available facts and figures and, train the computers to get into a self-instructed manner without explicit programming. The more the information is fed, the more the computers tend to learn, and can develop by themselves based on analyzing the statistics. It generates precise results and analysis by developing data-driven models and efficient algorithms for real-time processing of the numbers.
ML programs have become popular owing to their growing usage in mainstream commercial applications such as financial management, security improvements, enhanced automation user behavior analysis and, cognitive services. With growing adoption of such models, the Machine learning operations are required to successfully utilize AI in different industries.
MLOps comprises of a set of practices for association and communication between the operations professionals and data scientists. With the execution of such practices, the management process is simplified which in turn brings the organizational efficiency. The process of operationalizing ML is a complex process that incorporates a continual loop of number of tasks statistics collection and categorization, research to improve the performance, model validation and, monitoring of performance and retraining the model in case of any inefficiency.
Induced Investments in AI/ML-Based Systems Is Key Trend
With the objective of gaining competitive advantage through better customer insights, boosted employee productivity, businesses across all industries globally are induced towards increasing their investment in AI/ML systems.
Such automation systems can quickly and accurately analyze data and use complex algorithms to predict future steps, that eventually enhances the productivity of the enterprises.
MLOps helps monitor AI models in production, manage performance changes, and retrain them as needed, to keep them accurate over time.
Moreover, these systems have revolutionized the business management by consolidating workflow management tools and trend forecasting.
Surge in investment in machine learning remarkably contributes toward the demand expansion.
In July 2024, the Union Government allocated INR 551.75 crore to the IndiaAI Mission, showing its strong support for AI research and use in the country.
The goal is to make India a global leader in artificial intelligence.
Furthermore, a large number of small start-ups and tech organizations have invested in the embracing of non–proprietary MLOps platforms to enhance efficiency in their value chains, which propels the growth at global level.
Moreover, increase in availability of affordable, high-quality automated technology is expected to drive growth expansion in coming years.
The U.S. Department of Defense (DoD) significantly increased the value of its AI-related federal contracts from USD 355 million in the year leading up to August 2022 to USD 4.6 billion by August 2023.
This surge reflects a strategic emphasis on AI technologies, including MLOps, to enhance defense capabilities.
Integration of DevOps with Machine Learning Is Main Growth Driver
DevOps integrated with ML automates code and model testing, ensuring that each new data science script or model version is properly validated before being merged into the production pipeline.
MLOps enhances collaboration between data scientists, ML engineers, and operations teams, much like DevOps did for developers and IT ops.
The U.K.'s Advanced Research and Invention Agency (ARIA) has an initial budget of GBP 800 million to fund high-risk, high-reward scientific research.
While not exclusively focused on MLOps, ARIA's funding supports innovative AI research that can contribute to advancements in MLOps practices.
DevOps tools such as version control help track ML work, making it easier to repeat results and trace changes.
As similar to devOps, MLOps automates the full ML process, including data preparation, model training, deployment, and performance monitoring.
Using DevOps methods helps bring order and consistency to the trial-and-error process of ML development.
Making ML processes more consistent helps in building trust, work faster, and improve quality all important for businesses to use AI.
The platform category held the larger market share, of 75%, in 2024, and it will grow at the highest CAGR, of approx. 33%, during the forecast period. This is because it provides the organization help in building, training, managing, and deploying the models in a production-ready ML environment. It accelerates the business experiments with purpose-built tools, including classification, data preparation, training and tuning, monitoring, and various other activities. Further, it helps to improve the workflow from stats collection to application deployment in the real world, range from small-scale to enterprise-level cloud and open-source platforms.
Another reason behind the demand of MLOps platforms is the scalability and enhanced collaboration it provides in the organizational processes. Machine learning scalability involves scaling of ML applications that can handle any amount of information and execute many computations in a profitable and efficient way to instantly serve millions of users worldwide. Such platforms can be used by businesses with growing plans, rapid upscaling, or large amounts of figures to help them clean and prepare an insightful data.
The components analyzed here are:
Platform (Larger and Faster-Growing Category)
Services
Deployment Model Analysis
The on-premises category held the largest market share, of 50%, in 2024. This is because industries with strict rules, such as healthcare and finance, need to keep their data safe and private. On-premises make sure sensitive data stays inside the company and does not go to the cloud. Big companies also stick with on-premises setups because moving to the cloud can be expensive, complicated, and hard to manage under strict legal rules, such as HIPAA and GDPR.
The deployment models analyzed here are:
On-premise (Largest Category)
Cloud (Fastest-Growing Category)
Hybrid
Application Analysis
The data management category held the larger market share, of 65%, in 2024. This is because data management is crucial for all ML projects, with long-standing investments in data lakes, warehouses, and ETL pipelines forming the foundation. Data platforms support multiple departments beyond data science, such as finance, HR, and marketing. This expands the total addressable market across business intelligence, reporting, and compliance to drive sustained, high-value investment.
The applications analyzed here are:
Data Management (Larger Category)
Modeling (Faster-Growing Category)
End User Analysis
The ML engineers category will grow at the highest CAGR, of 45%, during the forecast period. As companies advance in their use of AI from experimenting with models to actually deploying and maintaining them, the demand for ML engineers is growing. These engineers focus on getting models into production, ensuring they run smoothly, and making them scalable, using techniques from software engineering and DevOps. Modern MLOps platforms, which now focus on automation, version control, and continuous integration, are designed to support the work of ML engineers.
The end users analyzed here are:
Data Scientists (Largest Category)
ML Engineers (Fastest-Growing Category)
AI enthusiasts
Data Engineers
Industry Analysis
The BFSI category held the largest market share, of 40%, in 2024. This is because it has long been a leader in adopting data-driven technologies, including AI and ML. Financial institutions use ML extensively for fraud detection, risk management, and understanding customers and the topography of trading strategies. In addition, the high volume of transactions and data in this segment drives the usage of ML.
The healthcare category will grow at the highest CAGR, of approx. 32%, during the forecast period. This is because of the increasing use of AI and ML in predictive analytics, personalized medicine, diagnostic tools, and drug discovery. As healthcare institutions look to improve efficiency, reduce costs, and enhance patient outcomes, the demand for advanced MLOps tools that help in deploying, scaling, and monitoring models in clinical settings is soaring. Additionally, the increasing volume of healthcare data and regulatory changes in health data privacy are fueling MLOps investments in the industry.
The industries analyzed here are:
BFSI (Largest Category)
Manufacturing
IT and Telecom
Smart Mobility
Retail and E-commerce
Energy and Utility
Healthcare (Fastest-Growing Category)
Media and Entertainment
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North America held the largest market share, of 45%, in 2024. The reason being strong R&D competences in AI in the developed economies and various leading AI companies and research institutes are based in this region. The increasing investment in advanced technologies to enhance the business operations and the customer experience are anticipated to provide lucrative growth opportunities in North America. These countries have strong research and development capabilities in AI over the past few years and invested heavily in AI-related technologies over the past few years. Also, they have implemented policies to support the development of the field.
The APAC region will grow at the highest CAGR, during the forecast period. This is because China, India, Japan and South Korea are embracing AI & ML technologies across the manufacturing, healthcare, finance, and retail sectors. Governments here are focusing on AI and digital transformation, with programs such as China’s AI Development Plan and India’s emphasis on AI in agriculture and healthcare.
The regions analyzed in this report are:
North America (Largest Region)
U.S. (Largest Region)
Canada (Fastest-Growing Region)
Europe
Germany (Largest Region)
U.K. (Fastest-Growing Region)
France
Italy
Russia
Rest of Europe
Asia-Pacific (Fastest-Growing Region)
China (Largest Region)
India (Fastest-Growing Region)
Japan
South Korea
Australia
Rest of APAC
Latin America
Brazil (Largest Region)
Mexico (Fastest-Growing Region)
Rest of LATAM
Middle East and Africa
Saudi Arabia
South Africa (Fastest-Growing Region)
U.A.E. (Largest Region)
Rest of MEA
Machine Learning Operations Market Share Analysis
The market is consolidated in nature because a few large players hold a significant portion. Big companies have established strong brand recognition, vast resources, and deep knowledge in AI and cloud technology. This helps them stay ahead in the market with complete solutions, including cloud services, data tools, and advanced AI features. This makes it hard for smaller companies to compete. It is also tough for new businesses to enter this field because building a good MLOps platform takes a lot of money, time, and technical skills. Big companies can afford to create full systems that cover everything from building AI models to using and monitoring them.
In March 2025, JFrog Ltd. launched JFrog ML, an MLOps solution, for development teams, ML engineers, and data scientists to develop and deploy AI applications in enterprises quickly and at scale.
In October 2024, Simplismart received an investment of USD 7 million in its series A funding round from Accel, Shastra VC, Titan Capital, and a few angel investors, to scale its MLOps platform for enterprises.
In July 2024, Rio Tinto announced that it is using MLOps to achieve sustainability, safety, and predictive maintenance in its mining operations.
In June 2024, JFrog Ltd. announced plans to acquire Qwak AI Ltd. to offer users of security, DevOps, and MLOps tools a unified solution.
In April 2024, Microsoft Corporation added a new tool, called Model Data Collector (MDC), to Azure Machine Learning. This feature aids in compliance, auditing, and monitoring of machine learning models.
In January 2024, TIER IV launched the Co-MLOps project to advance AI technologies for autonomous driving.
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