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 MLOps Technology market was valued at US$ million in 2023 and is anticipated to reach US$ million by 2030, witnessing a CAGR of % during the forecast period 2024-2030.
North American market for MLOps Technology is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
Asia-Pacific market for MLOps Technology is estimated to increase from $ million in 2023 to reach $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The global market for MLOps Technology in BFSI is estimated to increase from $ million in 2023 to $ million by 2030, at a CAGR of % during the forecast period of 2024 through 2030.
The major global companies of MLOps Technology include Microsoft, Amazon, Google, IBM, Dataiku, Lguazio, Databricks, DataRobot, Inc. and Cloudera, etc. In 2023, the world's top three vendors accounted for approximately % of the revenue.
This report aims to provide a comprehensive presentation of the global market for MLOps Technology, 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 MLOps Technology.
Report Scope
The MLOps Technology 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 MLOps Technology 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 MLOps Technology 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.
麻豆原创 Segmentation
By Company
Microsoft
Amazon
Google
IBM
Dataiku
Lguazio
Databricks
DataRobot, Inc.
Cloudera
Modzy
Algorithmia
HPE
Valohai
Allegro AI
Comet
FloydHub
Paperpace
Cnvrg.io
Segment by Type
On-premise
Cloud
Hybrid
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 MLOps Technology companies鈥� 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.
Please Note - This is an on demand report and will be delivered in 2 business days (48 hours) post payment.
1 Report Overview
1.1 Study Scope
1.2 麻豆原创 Analysis by Type
1.2.1 Global MLOps Technology 麻豆原创 Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 On-premise
1.2.3 Cloud
1.2.4 Hybrid
1.3 麻豆原创 by Application
1.3.1 Global MLOps Technology 麻豆原创 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 MLOps Technology 麻豆原创 Perspective (2019-2030)
2.2 MLOps Technology Growth Trends by Region
2.2.1 Global MLOps Technology 麻豆原创 Size by Region: 2019 VS 2023 VS 2030
2.2.2 MLOps Technology Historic 麻豆原创 Size by Region (2019-2024)
2.2.3 MLOps Technology Forecasted 麻豆原创 Size by Region (2025-2030)
2.3 MLOps Technology 麻豆原创 Dynamics
2.3.1 MLOps Technology Industry Trends
2.3.2 MLOps Technology 麻豆原创 Drivers
2.3.3 MLOps Technology 麻豆原创 Challenges
2.3.4 MLOps Technology 麻豆原创 Restraints
3 Competition Landscape by Key Players
3.1 Global Top MLOps Technology Players by Revenue
3.1.1 Global Top MLOps Technology Players by Revenue (2019-2024)
3.1.2 Global MLOps Technology Revenue 麻豆原创 Share by Players (2019-2024)
3.2 Global MLOps Technology 麻豆原创 Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by MLOps Technology Revenue
3.4 Global MLOps Technology 麻豆原创 Concentration Ratio
3.4.1 Global MLOps Technology 麻豆原创 Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by MLOps Technology Revenue in 2023
3.5 MLOps Technology Key Players Head office and Area Served
3.6 Key Players MLOps Technology Product Solution and Service
3.7 Date of Enter into MLOps Technology 麻豆原创
3.8 Mergers & Acquisitions, Expansion Plans
4 MLOps Technology Breakdown Data by Type
4.1 Global MLOps Technology Historic 麻豆原创 Size by Type (2019-2024)
4.2 Global MLOps Technology Forecasted 麻豆原创 Size by Type (2025-2030)
5 MLOps Technology Breakdown Data by Application
5.1 Global MLOps Technology Historic 麻豆原创 Size by Application (2019-2024)
5.2 Global MLOps Technology Forecasted 麻豆原创 Size by Application (2025-2030)
6 North America
6.1 North America MLOps Technology 麻豆原创 Size (2019-2030)
6.2 North America MLOps Technology 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America MLOps Technology 麻豆原创 Size by Country (2019-2024)
6.4 North America MLOps Technology 麻豆原创 Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe MLOps Technology 麻豆原创 Size (2019-2030)
7.2 Europe MLOps Technology 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe MLOps Technology 麻豆原创 Size by Country (2019-2024)
7.4 Europe MLOps Technology 麻豆原创 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 MLOps Technology 麻豆原创 Size (2019-2030)
8.2 Asia-Pacific MLOps Technology 麻豆原创 Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific MLOps Technology 麻豆原创 Size by Region (2019-2024)
8.4 Asia-Pacific MLOps Technology 麻豆原创 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 MLOps Technology 麻豆原创 Size (2019-2030)
9.2 Latin America MLOps Technology 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America MLOps Technology 麻豆原创 Size by Country (2019-2024)
9.4 Latin America MLOps Technology 麻豆原创 Size by Country (2025-2030)
9.5 Mexico
9.6 Brazil
10 Middle East & Africa
10.1 Middle East & Africa MLOps Technology 麻豆原创 Size (2019-2030)
10.2 Middle East & Africa MLOps Technology 麻豆原创 Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa MLOps Technology 麻豆原创 Size by Country (2019-2024)
10.4 Middle East & Africa MLOps Technology 麻豆原创 Size by Country (2025-2030)
10.5 Turkey
10.6 Saudi Arabia
10.7 UAE
11 Key Players Profiles
11.1 Microsoft
11.1.1 Microsoft Company Detail
11.1.2 Microsoft Business Overview
11.1.3 Microsoft MLOps Technology Introduction
11.1.4 Microsoft Revenue in MLOps Technology Business (2019-2024)
11.1.5 Microsoft Recent Development
11.2 Amazon
11.2.1 Amazon Company Detail
11.2.2 Amazon Business Overview
11.2.3 Amazon MLOps Technology Introduction
11.2.4 Amazon Revenue in MLOps Technology Business (2019-2024)
11.2.5 Amazon Recent Development
11.3 Google
11.3.1 Google Company Detail
11.3.2 Google Business Overview
11.3.3 Google MLOps Technology Introduction
11.3.4 Google Revenue in MLOps Technology Business (2019-2024)
11.3.5 Google Recent Development
11.4 IBM
11.4.1 IBM Company Detail
11.4.2 IBM Business Overview
11.4.3 IBM MLOps Technology Introduction
11.4.4 IBM Revenue in MLOps Technology Business (2019-2024)
11.4.5 IBM Recent Development
11.5 Dataiku
11.5.1 Dataiku Company Detail
11.5.2 Dataiku Business Overview
11.5.3 Dataiku MLOps Technology Introduction
11.5.4 Dataiku Revenue in MLOps Technology Business (2019-2024)
11.5.5 Dataiku Recent Development
11.6 Lguazio
11.6.1 Lguazio Company Detail
11.6.2 Lguazio Business Overview
11.6.3 Lguazio MLOps Technology Introduction
11.6.4 Lguazio Revenue in MLOps Technology Business (2019-2024)
11.6.5 Lguazio Recent Development
11.7 Databricks
11.7.1 Databricks Company Detail
11.7.2 Databricks Business Overview
11.7.3 Databricks MLOps Technology Introduction
11.7.4 Databricks Revenue in MLOps Technology Business (2019-2024)
11.7.5 Databricks Recent Development
11.8 DataRobot, Inc.
11.8.1 DataRobot, Inc. Company Detail
11.8.2 DataRobot, Inc. Business Overview
11.8.3 DataRobot, Inc. MLOps Technology Introduction
11.8.4 DataRobot, Inc. Revenue in MLOps Technology Business (2019-2024)
11.8.5 DataRobot, Inc. Recent Development
11.9 Cloudera
11.9.1 Cloudera Company Detail
11.9.2 Cloudera Business Overview
11.9.3 Cloudera MLOps Technology Introduction
11.9.4 Cloudera Revenue in MLOps Technology Business (2019-2024)
11.9.5 Cloudera Recent Development
11.10 Modzy
11.10.1 Modzy Company Detail
11.10.2 Modzy Business Overview
11.10.3 Modzy MLOps Technology Introduction
11.10.4 Modzy Revenue in MLOps Technology Business (2019-2024)
11.10.5 Modzy Recent Development
11.11 Algorithmia
11.11.1 Algorithmia Company Detail
11.11.2 Algorithmia Business Overview
11.11.3 Algorithmia MLOps Technology Introduction
11.11.4 Algorithmia Revenue in MLOps Technology Business (2019-2024)
11.11.5 Algorithmia Recent Development
11.12 HPE
11.12.1 HPE Company Detail
11.12.2 HPE Business Overview
11.12.3 HPE MLOps Technology Introduction
11.12.4 HPE Revenue in MLOps Technology Business (2019-2024)
11.12.5 HPE Recent Development
11.13 Valohai
11.13.1 Valohai Company Detail
11.13.2 Valohai Business Overview
11.13.3 Valohai MLOps Technology Introduction
11.13.4 Valohai Revenue in MLOps Technology Business (2019-2024)
11.13.5 Valohai Recent Development
11.14 Allegro AI
11.14.1 Allegro AI Company Detail
11.14.2 Allegro AI Business Overview
11.14.3 Allegro AI MLOps Technology Introduction
11.14.4 Allegro AI Revenue in MLOps Technology Business (2019-2024)
11.14.5 Allegro AI Recent Development
11.15 Comet
11.15.1 Comet Company Detail
11.15.2 Comet Business Overview
11.15.3 Comet MLOps Technology Introduction
11.15.4 Comet Revenue in MLOps Technology Business (2019-2024)
11.15.5 Comet Recent Development
11.16 FloydHub
11.16.1 FloydHub Company Detail
11.16.2 FloydHub Business Overview
11.16.3 FloydHub MLOps Technology Introduction
11.16.4 FloydHub Revenue in MLOps Technology Business (2019-2024)
11.16.5 FloydHub Recent Development
11.17 Paperpace
11.17.1 Paperpace Company Detail
11.17.2 Paperpace Business Overview
11.17.3 Paperpace MLOps Technology Introduction
11.17.4 Paperpace Revenue in MLOps Technology Business (2019-2024)
11.17.5 Paperpace Recent Development
11.18 Cnvrg.io
11.18.1 Cnvrg.io Company Detail
11.18.2 Cnvrg.io Business Overview
11.18.3 Cnvrg.io MLOps Technology Introduction
11.18.4 Cnvrg.io Revenue in MLOps Technology Business (2019-2024)
11.18.5 Cnvrg.io 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
Microsoft
Amazon
Google
IBM
Dataiku
Lguazio
Databricks
DataRobot, Inc.
Cloudera
Modzy
Algorithmia
HPE
Valohai
Allegro AI
Comet
FloydHub
Paperpace
Cnvrg.io
听
听
*If Applicable.
