MLOps is the process of taking an experimental Machine Learning model into a production system. The word is a compound of 鈥淢achine Learning鈥� 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 market for Machine Learning Operations (MLOps) was estimated to be worth US$ 545.5 million in 2023 and is forecast to a readjusted size of US$ 9066.7 million by 2030 with 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.
Report Scope
This report aims to provide a comprehensive presentation of the global market for Machine Learning Operations (MLOps), focusing on the total sales revenue, key companies market share and ranking, together with an analysis of Machine Learning Operations (MLOps) by region & country, by Type, and by Application.
The Machine Learning Operations (MLOps) market size, estimations, and forecasts are provided in terms of sales revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. 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).
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
U.K.
Italy
Russia
Asia-Pacific
China
Japan
South Korea
China Taiwan
Southeast Asia
India
Latin America
Mexico
Brazil
Argentina
Middle East & Africa
Turkey
Saudi Arabia
UAE
Chapter Outline
Chapter 1: Introduces the report scope of the report, global total market size. This chapter also provides the market dynamics, latest developments of the market, the driving factors and restrictive factors of the market, the challenges and risks faced by manufacturers in the industry, and the analysis of relevant policies in the industry.
Chapter 2: Detailed analysis of Machine Learning Operations (MLOps) manufacturers competitive landscape, revenue market share, latest development plan, merger, and acquisition information, etc.
Chapter 3: 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 4: 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 5: Revenue of Machine Learning Operations (MLOps) in regional level. It provides a quantitative analysis of the market size and development potential of each region and introduces the market development, future development prospects, market space, and market size of each country in the world.
Chapter 6: Revenue of Machine Learning Operations (MLOps) in country level. It provides sigmate data by Type, and by Application for each country/region.
Chapter 7: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product revenue, gross margin, product introduction, recent development, etc.
Chapter 8: Analysis of industrial chain, including the upstream and downstream of the industry.
Chapter 9: Conclusion.
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1 Market Overview
1.1 Machine Learning Operations (MLOps) Product Introduction
1.2 Global Machine Learning Operations (MLOps) Market Size Forecast
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 (2023)
2.2 Global Machine Learning Operations (MLOps) Revenue by Company (2019-2024)
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 (2019-2024)
2.6.2 Global 5 and 10 Largest Companies by Machine Learning Operations (MLOps) Revenue in 2023
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 2023)
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 (2019 VS 2023 VS 2030)
3.2.2 Global Machine Learning Operations (MLOps) Sales Value, by Type (2019-2030)
3.2.3 Global Machine Learning Operations (MLOps) Sales Value, by Type (%) (2019-2030)
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 (2019 VS 2023 VS 2030)
4.2.2 Global Machine Learning Operations (MLOps) Sales Value, by Application (2019-2030)
4.2.3 Global Machine Learning Operations (MLOps) Sales Value, by Application (%) (2019-2030)
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: 2019 VS 2023 VS 2030
5.1.2 Global Machine Learning Operations (MLOps) Sales Value by Region (2019-2024)
5.1.3 Global Machine Learning Operations (MLOps) Sales Value by Region (2025-2030)
5.1.4 Global Machine Learning Operations (MLOps) Sales Value by Region (%), (2019-2030)
5.2 North America
5.2.1 North America Machine Learning Operations (MLOps) Sales Value, 2019-2030
5.2.2 North America Machine Learning Operations (MLOps) Sales Value by Country (%), 2023 VS 2030
5.3 Europe
5.3.1 Europe Machine Learning Operations (MLOps) Sales Value, 2019-2030
5.3.2 Europe Machine Learning Operations (MLOps) Sales Value by Country (%), 2023 VS 2030
5.4 Asia Pacific
5.4.1 Asia Pacific Machine Learning Operations (MLOps) Sales Value, 2019-2030
5.4.2 Asia Pacific Machine Learning Operations (MLOps) Sales Value by Country (%), 2023 VS 2030
5.5 South America
5.5.1 South America Machine Learning Operations (MLOps) Sales Value, 2019-2030
5.5.2 South America Machine Learning Operations (MLOps) Sales Value by Country (%), 2023 VS 2030
5.6 Middle East & Africa
5.6.1 Middle East & Africa Machine Learning Operations (MLOps) Sales Value, 2019-2030
5.6.2 Middle East & Africa Machine Learning Operations (MLOps) Sales Value by Country (%), 2023 VS 2030
6 Segmentation by Key Countries/Regions
6.1 Key Countries/Regions Machine Learning Operations (MLOps) Sales Value Growth Trends, 2019 VS 2023 VS 2030
6.2 Key Countries/Regions Machine Learning Operations (MLOps) Sales Value
6.3 United States
6.3.1 United States Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.3.2 United States Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.3.3 United States Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
6.4 Europe
6.4.1 Europe Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.4.2 Europe Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.4.3 Europe Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
6.5 China
6.5.1 China Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.5.2 China Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.5.3 China Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
6.6 Japan
6.6.1 Japan Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.6.2 Japan Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.6.3 Japan Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
6.7 South Korea
6.7.1 South Korea Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.7.2 South Korea Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.7.3 South Korea Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
6.8 Southeast Asia
6.8.1 Southeast Asia Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.8.2 Southeast Asia Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.8.3 Southeast Asia Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
6.9 India
6.9.1 India Machine Learning Operations (MLOps) Sales Value, 2019-2030
6.9.2 India Machine Learning Operations (MLOps) Sales Value by Type (%), 2023 VS 2030
6.9.3 India Machine Learning Operations (MLOps) Sales Value by Application, 2023 VS 2030
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
7.3.5 Microsoft 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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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) & (2019-2024)
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
10.1.2 Data Source
10.2 Author Details
10.3 Disclaimer
IBM
DataRobot
SAS
Microsoft
Amazon
Google
Dataiku
Databricks
HPE
Lguazio
ClearML
Modzy
Comet
Cloudera
Paperpace
Valohai
听
听
*If Applicable.
