Global Cloud Machine Learning Operations (MLOps) Market Research Report 2024

Publication ID:

PB506247833

Publication Date:

June 21, 2024

Pages:

Publisher:

MRRPB5

Countries:

Global [1]

USD 2,900.00

IT Services [2]

Computing & Electronics [3]

Description:

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 Cloud Machine Learning Operations (MLOps) market was valued at US$ 186.4 million in 2023 and is anticipated to reach US$ 3652.7 million by 2030, witnessing a CAGR of 44.6% during the forecast period 2024-2030.
North American market for Cloud Machine Learning Operations (MLOps) 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 Cloud Machine Learning Operations (MLOps) 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 Cloud Machine Learning Operations (MLOps) 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 Cloud Machine Learning Operations (MLOps) include IBM, DataRobot, SAS, Microsoft, Amazon, Google, Dataiku, Databricks and HPE, 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 Cloud 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 Cloud Machine Learning Operations (MLOps).
Report Scope
The Cloud 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 Cloud 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 Cloud 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
Platform
Services
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 Cloud Machine Learning Operations (MLOps) 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.

Table Of Contents:

1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Global Cloud Machine Learning Operations (MLOps) Market Size Growth Rate by Type: 2019 VS 2023 VS 2030
1.2.2 Platform
1.2.3 Services
1.3 Market by Application
1.3.1 Global Cloud 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 Cloud Machine Learning Operations (MLOps) Market Perspective (2019-2030)
2.2 Cloud Machine Learning Operations (MLOps) Growth Trends by Region
2.2.1 Global Cloud Machine Learning Operations (MLOps) Market Size by Region: 2019 VS 2023 VS 2030
2.2.2 Cloud Machine Learning Operations (MLOps) Historic Market Size by Region (2019-2024)
2.2.3 Cloud Machine Learning Operations (MLOps) Forecasted Market Size by Region (2025-2030)
2.3 Cloud Machine Learning Operations (MLOps) Market Dynamics
2.3.1 Cloud Machine Learning Operations (MLOps) Industry Trends
2.3.2 Cloud Machine Learning Operations (MLOps) Market Drivers
2.3.3 Cloud Machine Learning Operations (MLOps) Market Challenges
2.3.4 Cloud Machine Learning Operations (MLOps) Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Cloud Machine Learning Operations (MLOps) Players by Revenue
3.1.1 Global Top Cloud Machine Learning Operations (MLOps) Players by Revenue (2019-2024)
3.1.2 Global Cloud Machine Learning Operations (MLOps) Revenue Market Share by Players (2019-2024)
3.2 Global Cloud Machine Learning Operations (MLOps) Market Share by Company Type (Tier 1, Tier 2, and Tier 3)
3.3 Players Covered: Ranking by Cloud Machine Learning Operations (MLOps) Revenue
3.4 Global Cloud Machine Learning Operations (MLOps) Market Concentration Ratio
3.4.1 Global Cloud Machine Learning Operations (MLOps) Market Concentration Ratio (CR5 and HHI)
3.4.2 Global Top 10 and Top 5 Companies by Cloud Machine Learning Operations (MLOps) Revenue in 2023
3.5 Cloud Machine Learning Operations (MLOps) Key Players Head office and Area Served
3.6 Key Players Cloud Machine Learning Operations (MLOps) Product Solution and Service
3.7 Date of Enter into Cloud Machine Learning Operations (MLOps) Market
3.8 Mergers & Acquisitions, Expansion Plans
4 Cloud Machine Learning Operations (MLOps) Breakdown Data by Type
4.1 Global Cloud Machine Learning Operations (MLOps) Historic Market Size by Type (2019-2024)
4.2 Global Cloud Machine Learning Operations (MLOps) Forecasted Market Size by Type (2025-2030)
5 Cloud Machine Learning Operations (MLOps) Breakdown Data by Application
5.1 Global Cloud Machine Learning Operations (MLOps) Historic Market Size by Application (2019-2024)
5.2 Global Cloud Machine Learning Operations (MLOps) Forecasted Market Size by Application (2025-2030)
6 North America
6.1 North America Cloud Machine Learning Operations (MLOps) Market Size (2019-2030)
6.2 North America Cloud Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
6.3 North America Cloud Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
6.4 North America Cloud Machine Learning Operations (MLOps) Market Size by Country (2025-2030)
6.5 United States
6.6 Canada
7 Europe
7.1 Europe Cloud Machine Learning Operations (MLOps) Market Size (2019-2030)
7.2 Europe Cloud Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
7.3 Europe Cloud Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
7.4 Europe Cloud 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 Cloud Machine Learning Operations (MLOps) Market Size (2019-2030)
8.2 Asia-Pacific Cloud Machine Learning Operations (MLOps) Market Growth Rate by Region: 2019 VS 2023 VS 2030
8.3 Asia-Pacific Cloud Machine Learning Operations (MLOps) Market Size by Region (2019-2024)
8.4 Asia-Pacific Cloud 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 Cloud Machine Learning Operations (MLOps) Market Size (2019-2030)
9.2 Latin America Cloud Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
9.3 Latin America Cloud Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
9.4 Latin America Cloud 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 Cloud Machine Learning Operations (MLOps) Market Size (2019-2030)
10.2 Middle East & Africa Cloud Machine Learning Operations (MLOps) Market Growth Rate by Country: 2019 VS 2023 VS 2030
10.3 Middle East & Africa Cloud Machine Learning Operations (MLOps) Market Size by Country (2019-2024)
10.4 Middle East & Africa Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.1.4 IBM Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.2.4 DataRobot Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.3.4 SAS Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.4.4 Microsoft Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.5.4 Amazon Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.6.4 Google Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.7.4 Dataiku Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.8.4 Databricks Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.9.4 HPE Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.10.4 Lguazio Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.11.4 ClearML Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.12.4 Modzy Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.13.4 Comet Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.14.4 Cloudera Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.15.4 Paperpace Revenue in Cloud 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 Cloud Machine Learning Operations (MLOps) Introduction
11.16.4 Valohai Revenue in Cloud 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

Companies Mentioned:

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

License Types:

Single User License (PDF)

This license allows for use of a publication by one person.
This person may print out a single copy of the publication.
This person can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.
This person cannot share the publication (or any information contained therein) with any other person or persons.
Unless a Enterprise License is purchased, a Single User License must be purchased for every person that wishes to use the publication within the same organization.
Customers who infringe these license terms are liable for a Global license fee.

听

Site License (PDF)*

This license allows for use of a publication by all users within one corporate location, e.g. a regional office.
These users may print out a single copy of the publication.
These users can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.
These users cannot share the publication (or any information contained therein) with any other person or persons outside the corporate location for which the publication is purchased.
Unless a Enterprise License is purchased, a Site User License must be purchased for every corporate location by an organization that wishes to use the publication within the same organization.
Customers who infringe these license terms are liable for a Global license fee.

听

Global License (PDF)*

This license allows for use of a publication by unlimited users within the purchasing organization e.g. all employees of a single company.
Each of these people may use the publication on any computer, and may print out the report, but may not share the publication (or any information contained therein) with any other person or persons outside of the organization.
These employees of purchasing organization can include information given in the publication in presentations and internal reports by providing full copyright credit to the publisher.

*If Applicable.

91制片厂