MLOps market size | Insights, Trends & Growth Analysis 2025

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Code: MTA10181

Publication Date: Oct 2025

What is the size of MLOps Market?

According to 6Wresearch internal database and industry insights, the Global MLOps Market was valued at USD 2.9 Billion in 2024 and is expected to reach USD 10.9 Billion by 2031, growing at a compound annual growth rate of 15.60% during the forecast period (2025-2031).

The primary catalysts for this increase are the growing prevalence of AI and machine learning technologies across industries, the need for operational efficiency in deploying ML models, and the increased need for real-time data analytics.

Growth Factors for MLOps Industry

A growing need to automate machine learning workflows to enhance operational efficiency.
Growing demand for real-time data analytics in various business functions.
Growing organisational investment in AI and machine learning projects.
Increased focus on scalability and agility in deploying machine learning models across industries.
Growing demand for tools that can manage and monitor complex machine learning systems.

MLOps Market Trends

The MLOps market is experiencing considerable trends, with the growing use of automation tools and technologies for machine learning (ML) model implementation and management. There is an increase in the integration of real-time data and monitoring of ML models. Organisations are deliberately choosing containerised environments or cloud MLOps platforms for scalability and efficiency. There is also a trend toward open-source tools and technologies in MLOps for cost reduction and collaboration.

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Emerging Developments in the MLOps Market

The MLOps market is evolving with developments such as the integration of advanced AI and machine learning technologies to improve predictive analytics and decision-making. The shift towards adopting edge computing for real-time model training and deployment is gaining momentum. Moreover, cloud-based MLOps platforms are expected to expand, enabling easier and cost-effective management of machine learning models. Companies are also focusing on integrating MLOps with DevOps and data science practices to enhance overall operational workflows.