The Market Reports

Call: +1-631-407-1315 / +91-750-729-1479
Email: sales@themarketreports.com

Global Machine Learning Operations (MLOps) Market Research Report 2025

Global Machine Learning Operations (MLOps) Market Research Report 2025

Publishing Date : Mar, 2025

License Type :
 

Report Code : 1902714

No of Pages : 94

Ask Your Query
Synopsis
MLOps is the process of taking an experimental Machine Learning model into a production system. The word is a compound of “Machine Learning” and the continuous development practice of DevOps in the software field. Machine Learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
The global Machine Learning Operations (MLOps) market was valued at US$ 545.5 million in 2023 and is anticipated to reach US$ 9066.7 million by 2030, witnessing a CAGR of 41.8% during the forecast period 2024-2030.
The key vendors providing Machine Learning Operations (MLOps) worldwide are IBM, DataRobot, SAS, Microsoft, Amazon, Google, Dataiku, Databricks, and others. The top five vendors together hold over 45% of the market share, with the largest producer being IBM with 10% of the market share. The major regions offering machine learning operations globally are North America, Europe, China, and the Middle East. In terms of their product categories, on-premise type have the highest market share at over 55%, followed by cloud MLOps at 35%. In terms of its applications, BFSI is its top application area, with over 25% market share, followed by the public sector and manufacturing.
This report aims to provide a comprehensive presentation of the global market for Machine Learning Operations (MLOps), with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding Machine Learning Operations (MLOps).
Report Scope
The Machine Learning Operations (MLOps) market size, estimations, and forecasts are provided in terms of revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global Machine Learning Operations (MLOps) market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the Machine Learning Operations (MLOps) companies, new entrants, and industry chain related companies in this market with information on the revenues, sales volume, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Segmentation
By Company
IBM
DataRobot
SAS
Microsoft
Amazon
Google
Dataiku
Databricks
HPE
Lguazio
ClearML
Modzy
Comet
Cloudera
Paperpace
Valohai
Segment by Type
On-premise
Cloud
Others
Segment by Application
BFSI
Healthcare
Retail
Manufacturing
Public Sector
Others
By Region
North America
United States
Canada
Europe
Germany
France
UK
Italy
Russia
Nordic Countries
Rest of Europe
Asia-Pacific
China
Japan
South Korea
Southeast Asia
India
Australia
Rest of Asia
Latin America
Mexico
Brazil
Rest of Latin America
Middle East & Africa
Turkey
Saudi Arabia
UAE
Rest of MEA
Chapter Outline
Chapter 1: Introduces the report scope of the report, executive summary of different market segments (by Type, by Application, etc), including the market size of each market segment, future development potential, and so on. It offers a high-level view of the current state of the market and its likely evolution in the short to mid-term, and long term.
Chapter 2: Introduces executive summary of global market size, regional market size, this section also introduces the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by companies in the industry, and the analysis of relevant policies in the industry.
Chapter 3: Detailed analysis of Machine Learning Operations (MLOps) companies’ competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 4: Provides the analysis of various market segments by Type, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 5: Provides the analysis of various market segments by Application, covering the market size and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 6, 7, 8, 9, 10: North America, Europe, Asia Pacific, Latin America, Middle East and Africa segment by country. It provides a quantitative analysis of the market size and development potential of each region and its main countries and introduces the market development, future development prospects, market space, and capacity of each country in the world.
Chapter 11: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product sales, revenue, price, gross margin, product introduction, recent development, etc.
Chapter 12: The main points and conclusions of the report.
Index
1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Machine Learning Operations (MLOps) Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 On-premise
1.2.3 Cloud
1.2.4 Others
1.3 Market by Application
1.3.1 Global Machine Learning Operations (MLOps) Market Growth by Application: 2019 VS 2023 VS 2030
1.3.2 BFSI
1.3.3 Healthcare
1.3.4 Retail
1.3.5 Manufacturing
1.3.6 Public Sector
1.3.7 Others
1.4 Study Objectives
1.5 Years Considered
1.6 Years Considered
2 Global Growth Trends
2.1 Global Machine Learning Operations (MLOps) Market Perspective (2019-2030)
2.2 Machine Learning Operations (MLOps) Growth Trends by Region
2.2.1 Global Machine Learning Operations (MLOps) Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 Machine Learning Operations (MLOps) Historic Market Size by Region (2019-2024)
2.2.3 Machine Learning Operations (MLOps) Forecasted Market Size by Region (2025-2030)
2.3 Machine Learning Operations (MLOps) Market Dynamics
2.3.1 Machine Learning Operations (MLOps) Industry Trends
2.3.2 Machine Learning Operations (MLOps) Market Drivers
2.3.3 Machine Learning Operations (MLOps) Market Challenges
2.3.4 Machine Learning Operations (MLOps) Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Machine Learning Operations (MLOps) Players by Revenue
3.1.1 Global Top Machine Learning Operations (MLOps) Players by Revenue (2019-2024)
3.1.2 Global Machine Learning Operations (MLOps) Revenue Market Share by Players (2019-2024)
3.2 Global Machine Learning Operations (MLOps) Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Machine Learning Operations (MLOps) Revenue
3.4 Global Machine Learning Operations (MLOps) Market Concentration Ratio
3.4.1 Global Machine Learning Operations (MLOps) Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Machine Learning Operations (MLOps) Revenue in 2023
3.5 Machine Learning Operations (MLOps) Key Players Head office and Area Served
3.6 Key Players Machine Learning Operations (MLOps) Product Solution and Service
3.7 Date of Enter into Machine Learning Operations (MLOps) Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Machine Learning Operations (MLOps) Breakdown Data by Type
4.1 Global Machine Learning Operations (MLOps) Historic Market Size by Type (2019-2024)
4.2 Global Machine Learning Operations (MLOps) Forecasted Market Size by Type (2025-2030)
5 Machine Learning Operations (MLOps) Breakdown Data by Application
5.1 Global Machine Learning Operations (MLOps) Historic Market Size by Application (2019-2024)
5.2 Global Machine Learning Operations (MLOps) Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America Machine Learning Operations (MLOps) Market Size (2019-2030)
6.2 North America Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
6.4 North America Machine Learning Operations (MLOps) Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Machine Learning Operations (MLOps) Market Size (2019-2030)
7.2 Europe Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
7.4 Europe Machine Learning Operations (MLOps) Market Size by Country (2025-2030)
7.5 Germany
7.6 France
7.7 U.K.
7.8 Italy
7.9 Russia
7.10 Nordic Countries
8 Asia-Pacific
8.1 Asia-Pacific Machine Learning Operations (MLOps) Market Size (2019-2030)
8.2 Asia-Pacific Machine Learning Operations (MLOps) Market Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Machine Learning Operations (MLOps) Market Size by Region (2019-2024)
8.4 Asia-Pacific Machine Learning Operations (MLOps) Market Size by Region (2025-2030)
8.5 China
8.6 Japan
8.7 South Korea
8.8 Southeast Asia
8.9 India
8.10 Australia
9 Latin America
9.1 Latin America Machine Learning Operations (MLOps) Market Size (2019-2030)
9.2 Latin America Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
9.4 Latin America Machine Learning Operations (MLOps) Market Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa Machine Learning Operations (MLOps) Market Size (2019-2030)
10.2 Middle East & Africa Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
10.4 Middle East & Africa Machine Learning Operations (MLOps) Market Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 IBM
11.1.1 IBM Company Detail
11.1.2 IBM Business Overview
11.1.3 IBM Machine Learning Operations (MLOps) Introduction
11.1.4 IBM Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.1.5 IBM Recent Development
11.2 DataRobot
11.2.1 DataRobot Company Detail
11.2.2 DataRobot Business Overview
11.2.3 DataRobot Machine Learning Operations (MLOps) Introduction
11.2.4 DataRobot Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.2.5 DataRobot Recent Development
11.3 SAS
11.3.1 SAS Company Detail
11.3.2 SAS Business Overview
11.3.3 SAS Machine Learning Operations (MLOps) Introduction
11.3.4 SAS Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.3.5 SAS Recent Development
11.4 Microsoft
11.4.1 Microsoft Company Detail
11.4.2 Microsoft Business Overview
11.4.3 Microsoft Machine Learning Operations (MLOps) Introduction
11.4.4 Microsoft Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.4.5 Microsoft Recent Development
11.5 Amazon
11.5.1 Amazon Company Detail
11.5.2 Amazon Business Overview
11.5.3 Amazon Machine Learning Operations (MLOps) Introduction
11.5.4 Amazon Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.5.5 Amazon Recent Development
11.6 Google
11.6.1 Google Company Detail
11.6.2 Google Business Overview
11.6.3 Google Machine Learning Operations (MLOps) Introduction
11.6.4 Google Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.6.5 Google Recent Development
11.7 Dataiku
11.7.1 Dataiku Company Detail
11.7.2 Dataiku Business Overview
11.7.3 Dataiku Machine Learning Operations (MLOps) Introduction
11.7.4 Dataiku Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.7.5 Dataiku Recent Development
11.8 Databricks
11.8.1 Databricks Company Detail
11.8.2 Databricks Business Overview
11.8.3 Databricks Machine Learning Operations (MLOps) Introduction
11.8.4 Databricks Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.8.5 Databricks Recent Development
11.9 HPE
11.9.1 HPE Company Detail
11.9.2 HPE Business Overview
11.9.3 HPE Machine Learning Operations (MLOps) Introduction
11.9.4 HPE Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.9.5 HPE Recent Development
11.10 Lguazio
11.10.1 Lguazio Company Detail
11.10.2 Lguazio Business Overview
11.10.3 Lguazio Machine Learning Operations (MLOps) Introduction
11.10.4 Lguazio Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.10.5 Lguazio Recent Development
11.11 ClearML
11.11.1 ClearML Company Detail
11.11.2 ClearML Business Overview
11.11.3 ClearML Machine Learning Operations (MLOps) Introduction
11.11.4 ClearML Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.11.5 ClearML Recent Development
11.12 Modzy
11.12.1 Modzy Company Detail
11.12.2 Modzy Business Overview
11.12.3 Modzy Machine Learning Operations (MLOps) Introduction
11.12.4 Modzy Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.12.5 Modzy Recent Development
11.13 Comet
11.13.1 Comet Company Detail
11.13.2 Comet Business Overview
11.13.3 Comet Machine Learning Operations (MLOps) Introduction
11.13.4 Comet Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.13.5 Comet Recent Development
11.14 Cloudera
11.14.1 Cloudera Company Detail
11.14.2 Cloudera Business Overview
11.14.3 Cloudera Machine Learning Operations (MLOps) Introduction
11.14.4 Cloudera Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.14.5 Cloudera Recent Development
11.15 Paperpace
11.15.1 Paperpace Company Detail
11.15.2 Paperpace Business Overview
11.15.3 Paperpace Machine Learning Operations (MLOps) Introduction
11.15.4 Paperpace Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.15.5 Paperpace Recent Development
11.16 Valohai
11.16.1 Valohai Company Detail
11.16.2 Valohai Business Overview
11.16.3 Valohai Machine Learning Operations (MLOps) Introduction
11.16.4 Valohai Revenue in Machine Learning Operations (MLOps) Business (2019-2024)
11.16.5 Valohai Recent Development
12 Analyst's Viewpoints/Conclusions
13 Appendix
13.1 Research Methodology
13.1.1 Methodology/Research Approach
13.1.2 Data Source
13.2 Disclaimer
13.3 Author Details

Published By : QY Research

Why ‘The Market Reports’

In Business: Pre Sales Support: Post Sales Support: Turn Around Time: Clients Served: Clients Assisted: Client Types: Payment: Partners with: Number of reports: Custom Research: Report Availability: Report Delivery Format: Security:

Payments Accepted From

Connect with us at

Complete Secure Website

SiteLock