Machine Learning Operation Technology Market was valued at USD 5.3 Billion in 2022 and is projected to reach USD 22.7 Billion by 2030, growing at a CAGR of 19.7% from 2024 to 2030.
During the period from 2018 to 2022, the adoption of MLOps saw rapid advancement, particularly in sectors such as healthcare, finance, and manufacturing. This was driven by the increasing volume of data and the need for advanced analytics. Key factors that propelled MLOps technology included cloud computing, containerization, and the rising demand for AI-powered automation. The global MLOps market saw an annual growth rate of over 25% during this period, as more organizations recognized the need to integrate machine learning models seamlessly into their operations.
The demand for MLOps has been growing steadily and is expected to continue its upward trajectory from 2023 to 2033. As businesses expand their AI capabilities and seek to automate their workflows, the adoption of MLOps technologies will accelerate. Data privacy regulations, enhanced computational power, and AI democratization are among the drivers fueling this growth. Furthermore, new advancements in cloud-native technologies, edge computing, and serverless architectures are expected to significantly shape the future MLOps landscape.
Key trends influencing the demand from 2023 to 2033 include the evolution of AutoML, the integration of MLOps with DevOps pipelines, and the growing use of ML model monitoring tools. These advancements will enable businesses to not only streamline operations but also improve the overall performance of machine learning models deployed in production environments. Moreover, organizations are focusing on MLOps to ensure transparency, fairness, and compliance within AI systems, aligning with stricter regulatory standards.
The potential market size for MLOps is projected to reach billions of dollars by 2033, driven by the continued demand for efficient, scalable, and automated AI solutions. In addition to the mainstream adoption across industries, specialized use cases in fields such as autonomous systems, robotics, and smart cities are expected to open new revenue streams for MLOps technology providers.
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The Machine Learning Operations (MLOps) Technology Market has witnessed significant growth from 2018 to 2022, with enterprises increasingly adopting machine learning (ML) models for their processes. MLOps has revolutionized how machine learning models are developed, deployed, and maintained across industries, contributing to efficiency, scalability, and innovation. By 2022, MLOps technology had become a core component of machine learning workflows, facilitating the automation and governance of ML lifecycle management.
Microsoft
Amazon
IBM
Dataiku
Lguazio
Databricks
DataRobot
Inc.
Cloudera
Modzy
Algorithmia
HPE
Valohai
Allegro AI
Comet
FloydHub
Paperpace
Cnvrg.io
By the year 2030, the scale for growth in the market research industry is reported to be above 120 billion which further indicates its projected compound annual growth rate (CAGR), of more than 5.8% from 2023 to 2030. There have also been disruptions in the industry due to advancements in machine learning, artificial intelligence and data analytics There is predictive analysis and real time information about consumers which such technologies provide to the companies enabling them to make better and precise decisions. The Asia-Pacific region is expected to be a key driver of growth, accounting for more than 35% of total revenue growth. In addition, new innovative techniques such as mobile surveys, social listening, and online panels, which emphasize speed, precision, and customization, are also transforming this particular sector.
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Growing demand for below applications around the world has had a direct impact on the growth of the Global Machine Learning Operation Technology Market
BFSI
Healthcare
Retail
Manufacturing
Public Sector
Others
Based on Types the Market is categorized into Below types that held the largest Machine Learning Operation Technology market share In 2023.
On-premise
Cloud
Global (United States, Global and Mexico)
Europe (Germany, UK, France, Italy, Russia, Turkey, etc.)
Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)
South America (Brazil, Argentina, Columbia, etc.)
Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)
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1. Introduction of the Global Machine Learning Operation Technology Market
Overview of the Market
Scope of Report
Assumptions
2. Executive Summary
3. Research Methodology of Verified Market Reports
Data Mining
Validation
Primary Interviews
List of Data Sources
4. Global Machine Learning Operation Technology Market Outlook
Overview
Market Dynamics
Drivers
Restraints
Opportunities
Porters Five Force Model
Value Chain Analysis
5. Global Machine Learning Operation Technology Market, By Type
6. Global Machine Learning Operation Technology Market, By Application
7. Global Machine Learning Operation Technology Market, By Geography
Global
Europe
Asia Pacific
Rest of the World
8. Global Machine Learning Operation Technology Market Competitive Landscape
Overview
Company Market Ranking
Key Development Strategies
9. Company Profiles
10. Appendix
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