Machine Learning Operations (MLOps) Market to Skyrocket to USD 22.52 Billion by 2031, Growing at 38.3% CAGR | IBM, DataRobot, SAS, Microsoft, Amazon, Google

QY Research has recently published a report, titled "Machine Learning Operations (MLOps) Market Share and Ranking, Overall Sales and Demand Forecast 2025-2031". The global Machine Learning Operations (MLOps) market is comprehensively analyzed in the report with the main objective of providing accurate market data and useful recommendations to help players to gain strong growth in future. The report is compiled by subject matter experts and experienced market analysts, which makes it highly authentic and reliable. Readers are provided with deep analysis of historical and future market scenarios to get sound understanding of market competition and other important aspects. The report offers exhaustive research on market dynamics, key segments, leading players, and different regional markets. It is a complete package of thorough analysis and research on the global Machine Learning Operations (MLOps) market.

The global market for Machine Learning Operations (MLOps) was estimated to be worth US$ 1976 million in 2024 and is forecast to a readjusted size of US$ 22517 million by 2031 with a CAGR of 38.3% during the forecast period 2025-2031.

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Currently, the MLOps market is undergoing rapid development. With the acceleration of digital transformation across industries worldwide and the increasing application of artificial intelligence and machine learning technologies, the importance of MLOps is becoming increasingly evident. The market exhibits the following characteristics:

Wide-ranging application areas: In the financial sector, MLOps helps banks and insurance companies optimize risk assessment models and improve fraud detection efficiency; in the healthcare industry, MLOps enables disease prediction and assists in medical imaging diagnosis; in the retail sector, MLOps is used for precision marketing and inventory management optimization; and in manufacturing, MLOps is employed to enhance quality control and predict equipment failures. The active exploration and application of MLOps across industries are driving the continuous expansion of the market size.

Competitive landscape gradually taking shape: In the market, large cloud computing providers such as AWS, Google Cloud, and Microsoft Azure are entering the MLOps field leveraging their robust cloud infrastructure and rich AI service ecosystems; companies specializing in machine learning platforms, such as DataRobot and H2O.ai, possess deep technical expertise in MLOps solutions; simultaneously, emerging startups are continuously emerging, distinguishing themselves in niche markets through innovative technologies and unique service models. The overall competitive landscape is becoming increasingly diversified, with companies vying for market share through product innovation, strategic partnerships, and mergers and acquisitions.

Diverse demand drivers: On one hand, businesses have an urgent need to improve the efficiency of machine learning project development and reduce the time required to deploy models. Traditional machine learning projects often face challenges such as lengthy development cycles, difficulties in model deployment, and high maintenance costs. MLOps provides automated processes and standardized tools that can effectively address these pain points. On the other hand, with the explosive growth of data volume and the increasing complexity of models, companies need more specialized technical means to manage the entire model lifecycle and ensure the reliability and stability of model performance. Additionally, the need for cross-departmental collaboration has prompted companies to adopt MLOps to break down communication barriers between data science teams and IT operations teams, enabling efficient collaboration.

Trends

Deep integration with cloud-native technologies: In the future, MLOps will become more closely integrated with cloud-native technologies. Cloud-native architectures (such as containerization technology Docker and container orchestration tools like Kubernetes) provide MLOps with efficient resource management, flexible deployment methods, and robust scalability. By leveraging cloud-native technologies, enterprises can easily achieve rapid deployment and migration of machine learning models across different cloud environments or hybrid cloud environments, significantly reducing infrastructure management costs while enhancing the overall resilience and reliability of the system.

Continuously improving automation: Automation is one of the core development directions of MLOps. From data collection, cleaning, and labeling, to model training, tuning, and evaluation, to model deployment and monitoring, each link will achieve a higher degree of automation. For example, automated machine learning (AutoML) technology will further develop, enabling the automatic selection of the optimal algorithms, parameter configurations, and data preprocessing methods, greatly reducing manual intervention and improving the development efficiency of machine learning projects. At the same time, event-driven automated processes will monitor model performance in real time. When model performance deviates from expectations or data distribution changes, the system will automatically trigger model retraining or adjustments to ensure the model maintains optimal performance.

Emphasis on model explainability and compliance: As machine learning models are widely adopted in critical business domains such as finance, healthcare, and law, model explainability and compliance have become key concerns. Future MLOps platforms will integrate more explainability tools to help users understand the decision-making process and output results of models, thereby enhancing trust in the models. Additionally, in terms of data privacy protection and regulatory compliance, MLOps will provide more comprehensive solutions to ensure that enterprises strictly adhere to relevant laws and regulations when using machine learning technologies, such as the European Union's General Data Protection Regulation (GDPR).

The Rise of Edge MLOps: With the widespread adoption of IoT devices and increasing demand for real-time data analysis and processing, edge computing is gaining increasing attention in the field of machine learning. Edge MLOps aims to extend the deployment and operation of machine learning models from the cloud to edge devices, enabling rapid local data processing and decision-making. This not only reduces data transmission latency and network bandwidth consumption but also enhances data security and privacy. In the future, edge MLOps will become an important growth area in the MLOps market, with related technologies and products continuously emerging to meet the diverse application needs of machine learning in edge scenarios across various industries.

The competitive landscape of the global Machine Learning Operations (MLOps) market is broadly studied in the report with large focus on recent developments, future plans of top players, and key growth strategies adopted by them. The analysts authoring the report have profiled almost every major player of the global Machine Learning Operations (MLOps) market and thrown light on their crucial business aspects such as production, areas of operation, and product portfolio.

Key Players Mentioned in the Global Machine Learning Operations (MLOps) Market Research Report:

IBM
DataRobot
SAS
Microsoft
Amazon
Google
Dataiku
Databricks
HPE
Lguazio
ClearML
Modzy
Comet
Cloudera
Paperpace
Valohai

The report offers great insights into important segments of the global Machine Learning Operations (MLOps) market while concentrating on their CAGR, market size, market share, and future growth potential. The global Machine Learning Operations (MLOps) market is mainly segmented according to type of product, application, and region. Each segment in these categories is extensively researched to become familiar with their growth prospects and key trends. Segmental analysis is highly important to identify key growth pockets of a global market. The report provides specific information on the market growth and demand of different products and applications to help players to focus on profitable areas of the global Machine Learning Operations (MLOps) market.

Details of Machine Learning Operations (MLOps) Market Segmentation: -

Segment by Type:

On-premise
Cloud
Others

Segment by Application:

BFSI
Healthcare
Retail
Manufacturing
Public Sector
Others

Research Methodology

This report has been compiled using latest primary and secondary research methodologies such as data triangulation, qualitative and quantitative analysis, and top-down and bottom-up approaches. The researchers also referred to reliable secondary sources for collecting important data and statistics. All the findings of the research study have been verified and revalidated with the help of authentic primary sources.

The researchers create a unique model and customize it for each research study. Their market forecasts are based on the industry-best combination of domain expertise, industry knowledge and experience, technological analysis, and economic tools. Technological market models are generally adopted for long-term market forecasting, whereas econometric models are adopted for short-term forecasting. The researchers were provided with advanced analysis tools at the time of the preparation of the report.

Report Objectives

(1) To carefully analyze and estimate the size of the global Machine Learning Operations (MLOps) market

(2) To clearly segment the global Machine Learning Operations (MLOps) market and estimate the market size of the segments

(3) To provide details about key strategies adopted by leading players of the global Machine Learning Operations (MLOps) market

(4) To help readers understand current and future market scenarios

(5) To provide information about latest trends of the global Machine Learning Operations (MLOps) market and its key segments

(6) To assess the contribution of each region or country to the global Machine Learning Operations (MLOps) market

(7) To provide information on important drivers, restraints, and opportunities of the global Machine Learning Operations (MLOps) market

(8) To accurately calculate the market shares of key segments, regions, and companies in the global Machine Learning Operations (MLOps) market

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Table of Content

1 Market Overview
1.1 Machine Learning Operations (MLOps) Product Introduction
1.2 Global Machine Learning Operations (MLOps) Market Size Forecast (2020-2031)
1.3 Machine Learning Operations (MLOps) Market Trends & Drivers
1.3.1 Machine Learning Operations (MLOps) Industry Trends
1.3.2 Machine Learning Operations (MLOps) Market Drivers & Opportunity
1.3.3 Machine Learning Operations (MLOps) Market Challenges
1.3.4 Machine Learning Operations (MLOps) Market Restraints
1.4 Assumptions and Limitations
1.5 Study Objectives
1.6 Years Considered
2 Competitive Analysis by Company
2.1 Global Machine Learning Operations (MLOps) Players Revenue Ranking (2024)
2.2 Global Machine Learning Operations (MLOps) Revenue by Company (2020-2025)
2.3 Key Companies Machine Learning Operations (MLOps) Manufacturing Base Distribution and Headquarters
2.4 Key Companies Machine Learning Operations (MLOps) Product Offered
2.5 Key Companies Time to Begin Mass Production of Machine Learning Operations (MLOps)
2.6 Machine Learning Operations (MLOps) Market Competitive Analysis
2.6.1 Machine Learning Operations (MLOps) Market Concentration Rate (2020-2025)
2.6.2 Global 5 and 10 Largest Companies by Machine Learning Operations (MLOps) Revenue in 2024
2.6.3 Global Top Companies by Company Type (Tier 1, Tier 2, and Tier 3) & (based on the Revenue in Machine Learning Operations (MLOps) as of 2024)
2.7 Mergers & Acquisitions, Expansion
3 Segmentation by Type
3.1 Introduction by Type
3.1.1 On-premise
3.1.2 Cloud
3.1.3 Others
3.2 Global Machine Learning Operations (MLOps) Sales Value by Type
3.2.1 Global Machine Learning Operations (MLOps) Sales Value by Type (2020 VS 2024 VS 2031)
3.2.2 Global Machine Learning Operations (MLOps) Sales Value, by Type (2020-2031)
3.2.3 Global Machine Learning Operations (MLOps) Sales Value, by Type (%) (2020-2031)
4 Segmentation by Application
4.1 Introduction by Application
4.1.1 BFSI
4.1.2 Healthcare
4.1.3 Retail
4.1.4 Manufacturing
4.1.5 Public Sector
4.1.6 Others
4.2 Global Machine Learning Operations (MLOps) Sales Value by Application
4.2.1 Global Machine Learning Operations (MLOps) Sales Value by Application (2020 VS 2024 VS 2031)
4.2.2 Global Machine Learning Operations (MLOps) Sales Value, by Application (2020-2031)
4.2.3 Global Machine Learning Operations (MLOps) Sales Value, by Application (%) (2020-2031)
5 Segmentation by Region
5.1 Global Machine Learning Operations (MLOps) Sales Value by Region
5.1.1 Global Machine Learning Operations (MLOps) Sales Value by Region: 2020 VS 2024 VS 2031
5.1.2 Global Machine Learning Operations (MLOps) Sales Value by Region (2020-2025)
5.1.3 Global Machine Learning Operations (MLOps) Sales Value by Region (2026-2031)
5.1.4 Global Machine Learning Operations (MLOps) Sales Value by Region (%), (2020-2031)
5.2 North America
5.2.1 North America Machine Learning Operations (MLOps) Sales Value, 2020-2031
5.2.2 North America Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
5.3 Europe
5.3.1 Europe Machine Learning Operations (MLOps) Sales Value, 2020-2031
5.3.2 Europe Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
5.4 Asia Pacific
5.4.1 Asia Pacific Machine Learning Operations (MLOps) Sales Value, 2020-2031
5.4.2 Asia Pacific Machine Learning Operations (MLOps) Sales Value by Region (%), 2024 VS 2031
5.5 South America
5.5.1 South America Machine Learning Operations (MLOps) Sales Value, 2020-2031
5.5.2 South America Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
5.6 Middle East & Africa
5.6.1 Middle East & Africa Machine Learning Operations (MLOps) Sales Value, 2020-2031
5.6.2 Middle East & Africa Machine Learning Operations (MLOps) Sales Value by Country (%), 2024 VS 2031
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Machine Learning Operations (MLOps) Sales Value Growth Trends, 2020 VS 2024 VS 2031
6.2 Key Countries/Regions Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.3 United States
6.3.1 United States Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.3.2 United States Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.3.3 United States Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
6.4 Europe
6.4.1 Europe Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.4.2 Europe Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.4.3 Europe Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
6.5 China
6.5.1 China Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.5.2 China Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.5.3 China Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
6.6 Japan
6.6.1 Japan Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.6.2 Japan Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.6.3 Japan Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
6.7 South Korea
6.7.1 South Korea Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.7.2 South Korea Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.7.3 South Korea Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
6.8 Southeast Asia
6.8.1 Southeast Asia Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.8.2 Southeast Asia Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.8.3 Southeast Asia Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
6.9 India
6.9.1 India Machine Learning Operations (MLOps) Sales Value, 2020-2031
6.9.2 India Machine Learning Operations (MLOps) Sales Value by Type (%), 2024 VS 2031
6.9.3 India Machine Learning Operations (MLOps) Sales Value by Application, 2024 VS 2031
7 Company Profiles
7.1 IBM
7.1.1 IBM Profile
7.1.2 IBM Main Business
7.1.3 IBM Machine Learning Operations (MLOps) Products, Services and Solutions
7.1.4 IBM Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.1.5 IBM Recent Developments
7.2 DataRobot
7.2.1 DataRobot Profile
7.2.2 DataRobot Main Business
7.2.3 DataRobot Machine Learning Operations (MLOps) Products, Services and Solutions
7.2.4 DataRobot Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.2.5 DataRobot Recent Developments
7.3 SAS
7.3.1 SAS Profile
7.3.2 SAS Main Business
7.3.3 SAS Machine Learning Operations (MLOps) Products, Services and Solutions
7.3.4 SAS Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.3.5 SAS Recent Developments
7.4 Microsoft
7.4.1 Microsoft Profile
7.4.2 Microsoft Main Business
7.4.3 Microsoft Machine Learning Operations (MLOps) Products, Services and Solutions
7.4.4 Microsoft Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.4.5 Microsoft Recent Developments
7.5 Amazon
7.5.1 Amazon Profile
7.5.2 Amazon Main Business
7.5.3 Amazon Machine Learning Operations (MLOps) Products, Services and Solutions
7.5.4 Amazon Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.5.5 Amazon Recent Developments
7.6 Google
7.6.1 Google Profile
7.6.2 Google Main Business
7.6.3 Google Machine Learning Operations (MLOps) Products, Services and Solutions
7.6.4 Google Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.6.5 Google Recent Developments
7.7 Dataiku
7.7.1 Dataiku Profile
7.7.2 Dataiku Main Business
7.7.3 Dataiku Machine Learning Operations (MLOps) Products, Services and Solutions
7.7.4 Dataiku Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.7.5 Dataiku Recent Developments
7.8 Databricks
7.8.1 Databricks Profile
7.8.2 Databricks Main Business
7.8.3 Databricks Machine Learning Operations (MLOps) Products, Services and Solutions
7.8.4 Databricks Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.8.5 Databricks Recent Developments
7.9 HPE
7.9.1 HPE Profile
7.9.2 HPE Main Business
7.9.3 HPE Machine Learning Operations (MLOps) Products, Services and Solutions
7.9.4 HPE Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.9.5 HPE Recent Developments
7.10 Lguazio
7.10.1 Lguazio Profile
7.10.2 Lguazio Main Business
7.10.3 Lguazio Machine Learning Operations (MLOps) Products, Services and Solutions
7.10.4 Lguazio Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.10.5 Lguazio Recent Developments
7.11 ClearML
7.11.1 ClearML Profile
7.11.2 ClearML Main Business
7.11.3 ClearML Machine Learning Operations (MLOps) Products, Services and Solutions
7.11.4 ClearML Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.11.5 ClearML Recent Developments
7.12 Modzy
7.12.1 Modzy Profile
7.12.2 Modzy Main Business
7.12.3 Modzy Machine Learning Operations (MLOps) Products, Services and Solutions
7.12.4 Modzy Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.12.5 Modzy Recent Developments
7.13 Comet
7.13.1 Comet Profile
7.13.2 Comet Main Business
7.13.3 Comet Machine Learning Operations (MLOps) Products, Services and Solutions
7.13.4 Comet Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.13.5 Comet Recent Developments
7.14 Cloudera
7.14.1 Cloudera Profile
7.14.2 Cloudera Main Business
7.14.3 Cloudera Machine Learning Operations (MLOps) Products, Services and Solutions
7.14.4 Cloudera Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.14.5 Cloudera Recent Developments
7.15 Paperpace
7.15.1 Paperpace Profile
7.15.2 Paperpace Main Business
7.15.3 Paperpace Machine Learning Operations (MLOps) Products, Services and Solutions
7.15.4 Paperpace Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.15.5 Paperpace Recent Developments
7.16 Valohai
7.16.1 Valohai Profile
7.16.2 Valohai Main Business
7.16.3 Valohai Machine Learning Operations (MLOps) Products, Services and Solutions
7.16.4 Valohai Machine Learning Operations (MLOps) Revenue (US$ Million) & (2020-2025)
7.16.5 Valohai Recent Developments
8 Industry Chain Analysis
8.1 Machine Learning Operations (MLOps) Industrial Chain
8.2 Machine Learning Operations (MLOps) Upstream Analysis
8.2.1 Key Raw Materials
8.2.2 Raw Materials Key Suppliers
8.2.3 Manufacturing Cost Structure
8.3 Midstream Analysis
8.4 Downstream Analysis (Customers Analysis)
8.5 Sales Model and Sales Channels
8.5.1 Machine Learning Operations (MLOps) Sales Model
8.5.2 Sales Channel
8.5.3 Machine Learning Operations (MLOps) Distributors
9 Research Findings and Conclusion
10 Appendix
10.1 Research Methodology
10.1.1 Methodology/Research Approach
10.1.1.1 Research Programs/Design
10.1.1.2 Market Size Estimation
10.1.1.3 Market Breakdown and Data Triangulation
10.1.2 Data Source
10.1.2.1 Secondary Sources
10.1.2.2 Primary Sources
10.2 Author Details
10.3 Disclaimer

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