Global and United States Machine Learning Operations (MLOps) Market Report & Forecast 2024-2034
Published on: 2024-01-04 | No of Pages : 400 | Industry : Software
Publisher : MRA | Format : PDF
Global and United States Machine Learning Operations (MLOps) Market Report & Forecast 2024-2034
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.
Market Analysis and InsightsGlobal and United States Machine Learning Operations (MLOps) Market
This report focuses on global and United States Machine Learning Operations (MLOps) market, also covers the segmentation data of other regions in regional level and county level.
The global Machine Learning Operations (MLOps) revenue was US$ 545.5 million in 2023 and is forecast to a readjusted size of US$ 9066.7 million by 2033 with a CAGR of 41.8% during the review period (2023-2033).
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.
Global Machine Learning Operations (MLOps) Scope and Market Size
Machine Learning Operations (MLOps) market is segmented in regional and country level, by players, by Type and by Application. Companies, stakeholders, and other participants in the global Machine Learning Operations (MLOps) market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application for the period 2018-2033.
For United States market, this report focuses on the Machine Learning Operations (MLOps) market size by players, by Type and by Application, for the period 2018-2033. The key players include the global and local players, which play important roles in United States.
IBM
DataRobot
SAS
Microsoft
Amazon
Google
Dataiku
Databricks
HPE
Lguazio
ClearML
Modzy
Comet
Cloudera
Paperpace
Valohai
Segment by Type
On-premise
Cloud
Others
BFSI
Healthcare
Retail
Manufacturing
Public Sector
Others
By Region
Americas
United States
Canada
Mexico
Brazil
China
APAC (excluding China)
Japan
South Korea
China Taiwan
ASEAN
India
EMEA
Europe
Middle East
Africa
Chapter 1Introduces Machine Learning Operations (MLOps) definition, global market size, United States market size, United States percentage in global market. 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 2Provides the analysis of various market segments by type, covering the revenue, and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 3Provides the analysis of various market segments by application, covering the revenue and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 4Detailed analysis of Machine Learning Operations (MLOps) companies’ competitive landscape, revenue, market share and industry ranking, latest development plan, merger and acquisition information, etc.
Chapter 5Revenue of Machine Learning Operations (MLOps) in global and 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 of each country in the world.
Chapter 6Americas by type, by application and by country revenue for each segment.
Chapter 7EMEA by type, by application and by region, revenue for each segment.
Chapter 8China by type, by application revenue for each segment.
Chapter 9APAC (excluding China) by type, by application and by region, revenue for each segment.
Chapter 10Provides profiles of key companies, introducing the basic situation of the main companies in the market in detail, including product descriptions and specifications, Machine Learning Operations (MLOps) revenue, gross margin and recent development, etc.
Chapter 11Analyst's Viewpoints/Conclusions
Market Analysis and InsightsGlobal and United States Machine Learning Operations (MLOps) Market
This report focuses on global and United States Machine Learning Operations (MLOps) market, also covers the segmentation data of other regions in regional level and county level.
The global Machine Learning Operations (MLOps) revenue was US$ 545.5 million in 2023 and is forecast to a readjusted size of US$ 9066.7 million by 2033 with a CAGR of 41.8% during the review period (2023-2033).
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.
Global Machine Learning Operations (MLOps) Scope and Market Size
Machine Learning Operations (MLOps) market is segmented in regional and country level, by players, by Type and by Application. Companies, stakeholders, and other participants in the global Machine Learning Operations (MLOps) market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on revenue and forecast by Type and by Application for the period 2018-2033.
For United States market, this report focuses on the Machine Learning Operations (MLOps) market size by players, by Type and by Application, for the period 2018-2033. The key players include the global and local players, which play important roles in United States.
By Company
IBM
DataRobot
SAS
Microsoft
Amazon
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
Americas
United States
Canada
Mexico
Brazil
China
APAC (excluding China)
Japan
South Korea
China Taiwan
ASEAN
India
EMEA
Europe
Middle East
Africa
Chapter Introduction
Chapter 1Introduces Machine Learning Operations (MLOps) definition, global market size, United States market size, United States percentage in global market. 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 2Provides the analysis of various market segments by type, covering the revenue, and development potential of each market segment, to help readers find the blue ocean market in different market segments.
Chapter 3Provides the analysis of various market segments by application, covering the revenue and development potential of each market segment, to help readers find the blue ocean market in different downstream markets.
Chapter 4Detailed analysis of Machine Learning Operations (MLOps) companies’ competitive landscape, revenue, market share and industry ranking, latest development plan, merger and acquisition information, etc.
Chapter 5Revenue of Machine Learning Operations (MLOps) in global and 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 of each country in the world.
Chapter 6Americas by type, by application and by country revenue for each segment.
Chapter 7EMEA by type, by application and by region, revenue for each segment.
Chapter 8China by type, by application revenue for each segment.
Chapter 9APAC (excluding China) by type, by application and by region, revenue for each segment.
Chapter 10Provides profiles of key companies, introducing the basic situation of the main companies in the market in detail, including product descriptions and specifications, Machine Learning Operations (MLOps) revenue, gross margin and recent development, etc.
Chapter 11Analyst's Viewpoints/Conclusions