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AI & Machine Learning Operationalization (MLOps) Software-Global Market Insights and Sales Trends 2025
Synopsis
AI & machine learning operationalization (MLOps) software allows users to manage and monitor machine learning models as they are integrated into business applications. In addition, many of these tools facilitate the deployment of models.
The global AI & Machine Learning Operationalization (MLOps) Software market size is expected to reach US$ million by 2029, growing at a CAGR of % from 2023 to 2029. The market is mainly driven by the significant applications of AI & Machine Learning Operationalization (MLOps) Software in various end use industries. The expanding demands from the SMEs and Large Enterprises, are propelling AI & Machine Learning Operationalization (MLOps) Software market. Artificial Intelligence Platforms, one of the segments analysed in this report, is projected to record % CAGR and reach US$ million by the end of the analysis period. Growth in the Chatbots segment is estimated at % CAGR for the next seven-year period.
As an important force driving a new round of scientific and technological revolution, artificial intelligence has been of national strategic importance. Many governments introduces polices and increase capital investment to support AI companies. The Digital Europe plan adopted by the European Union will allocate €9.2 billion on high-tech investments, such as supercomputing, artificial intelligence, and network security. In order to maintain its leading position, the United States will increase its investment in artificial intelligence research and development in non-defense fields, from US$1.6 billion to US$1.7 billion in 2022. According to the latest data released by IDC, global artificial intelligence revenue was US$432.8 billion in 2022, a year-on-year increase of 19.10%, including software, hardware and services.
Report Objectives
This report provides market insight on the different segments, by players, by Type, by Application. Market size and forecast (2018-2029) has been provided in the report. The primary objectives of this report are to provide 1) global market size and forecasts, growth rates, market dynamics, industry structure and developments, market situation, trends; 2) global market share and ranking by company; 3) comprehensive presentation of the global market for AI & Machine Learning Operationalization (MLOps) Software, with both quantitative and qualitative analysis through detailed segmentation; 4) detailed value chain analysis and review of growth factors essential for the existing market players and new entrants; 5) emerging opportunities in the market and the future impact of major drivers and restraints of the market.
Key Features of The Study:
This report provides in-depth analysis of the global AI & Machine Learning Operationalization (MLOps) Software market, and provides market size (US$ million) and CAGR for the forecast period (2023-2029), considering 2022 as the base year.
This report profiles key players in the global AI & Machine Learning Operationalization (MLOps) Software market based on the following parameters - company details (found date, headquarters, manufacturing bases), products portfolio, AI & Machine Learning Operationalization (MLOps) Software sales data, market share and ranking.
This report elucidates potential market opportunities across different segments and explains attractive investment proposition matrices for this market.
This report illustrates key insights about market drivers, restraints, opportunities, market trends, regional outlook.
Key companies of AI & Machine Learning Operationalization (MLOps) Software covered in this report include Google, Azure Machine Learning Studio, TensorFlow, H2O.AI, Cortana, IBM Watson, Salesforce Einstein, Infosys Nia and Amazon Alexa, etc.
The global AI & Machine Learning Operationalization (MLOps) Software market report caters to various stakeholders in this industry including investors, suppliers, product players, distributors, new entrants, and financial analysts.
Market Segmentation
Company Profiles:
Google
Azure Machine Learning Studio
TensorFlow
H2O.AI
Cortana
IBM Watson
Salesforce Einstein
Infosys Nia
Amazon Alexa
SiQ
Robin
Condeco
Global AI & Machine Learning Operationalization (MLOps) Software market, by region:
North America (U.S., Canada, Mexico)
Europe (Germany, France, UK, Italy, etc.)
Asia Pacific (China, Japan, South Korea, Southeast Asia, India, etc.)
South America (Brazil, etc.)
Middle East and Africa (Turkey, GCC Countries, Africa, etc.)
Global AI & Machine Learning Operationalization (MLOps) Software market, Segment by Type:
Artificial Intelligence Platforms
Chatbots
Deep Learning Software
Machine Learning Software
Global AI & Machine Learning Operationalization (MLOps) Software market, by Application
SMEs
Large Enterprises
Core Chapters
Chapter One: Introduces the report scope of the report, executive summary of global and regional market size and CAGR for the history and forecast period (2018-2023, 2024-2029). 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 Two: 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 Three: 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 Four: Detailed analysis of AI & Machine Learning Operationalization (MLOps) Software companies’ competitive landscape, revenue, market share and ranking, latest development plan, merger, and acquisition information, etc.
Chapter Five: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product introduction, revenue, recent development, etc.
Chapter Six, Seven, Eight, Nine and Ten: North America, Europe, Asia Pacific, Latin America, Middle East & Africa, revenue by country.
Chapter Eleven: 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 Twelve: Research Finding/Conclusion
AI & machine learning operationalization (MLOps) software allows users to manage and monitor machine learning models as they are integrated into business applications. In addition, many of these tools facilitate the deployment of models.
The global AI & Machine Learning Operationalization (MLOps) Software market size is expected to reach US$ million by 2029, growing at a CAGR of % from 2023 to 2029. The market is mainly driven by the significant applications of AI & Machine Learning Operationalization (MLOps) Software in various end use industries. The expanding demands from the SMEs and Large Enterprises, are propelling AI & Machine Learning Operationalization (MLOps) Software market. Artificial Intelligence Platforms, one of the segments analysed in this report, is projected to record % CAGR and reach US$ million by the end of the analysis period. Growth in the Chatbots segment is estimated at % CAGR for the next seven-year period.
As an important force driving a new round of scientific and technological revolution, artificial intelligence has been of national strategic importance. Many governments introduces polices and increase capital investment to support AI companies. The Digital Europe plan adopted by the European Union will allocate €9.2 billion on high-tech investments, such as supercomputing, artificial intelligence, and network security. In order to maintain its leading position, the United States will increase its investment in artificial intelligence research and development in non-defense fields, from US$1.6 billion to US$1.7 billion in 2022. According to the latest data released by IDC, global artificial intelligence revenue was US$432.8 billion in 2022, a year-on-year increase of 19.10%, including software, hardware and services.
Report Objectives
This report provides market insight on the different segments, by players, by Type, by Application. Market size and forecast (2018-2029) has been provided in the report. The primary objectives of this report are to provide 1) global market size and forecasts, growth rates, market dynamics, industry structure and developments, market situation, trends; 2) global market share and ranking by company; 3) comprehensive presentation of the global market for AI & Machine Learning Operationalization (MLOps) Software, with both quantitative and qualitative analysis through detailed segmentation; 4) detailed value chain analysis and review of growth factors essential for the existing market players and new entrants; 5) emerging opportunities in the market and the future impact of major drivers and restraints of the market.
Key Features of The Study:
This report provides in-depth analysis of the global AI & Machine Learning Operationalization (MLOps) Software market, and provides market size (US$ million) and CAGR for the forecast period (2023-2029), considering 2022 as the base year.
This report profiles key players in the global AI & Machine Learning Operationalization (MLOps) Software market based on the following parameters - company details (found date, headquarters, manufacturing bases), products portfolio, AI & Machine Learning Operationalization (MLOps) Software sales data, market share and ranking.
This report elucidates potential market opportunities across different segments and explains attractive investment proposition matrices for this market.
This report illustrates key insights about market drivers, restraints, opportunities, market trends, regional outlook.
Key companies of AI & Machine Learning Operationalization (MLOps) Software covered in this report include Google, Azure Machine Learning Studio, TensorFlow, H2O.AI, Cortana, IBM Watson, Salesforce Einstein, Infosys Nia and Amazon Alexa, etc.
The global AI & Machine Learning Operationalization (MLOps) Software market report caters to various stakeholders in this industry including investors, suppliers, product players, distributors, new entrants, and financial analysts.
Market Segmentation
Company Profiles:
Azure Machine Learning Studio
TensorFlow
H2O.AI
Cortana
IBM Watson
Salesforce Einstein
Infosys Nia
Amazon Alexa
SiQ
Robin
Condeco
Global AI & Machine Learning Operationalization (MLOps) Software market, by region:
North America (U.S., Canada, Mexico)
Europe (Germany, France, UK, Italy, etc.)
Asia Pacific (China, Japan, South Korea, Southeast Asia, India, etc.)
South America (Brazil, etc.)
Middle East and Africa (Turkey, GCC Countries, Africa, etc.)
Global AI & Machine Learning Operationalization (MLOps) Software market, Segment by Type:
Artificial Intelligence Platforms
Chatbots
Deep Learning Software
Machine Learning Software
Global AI & Machine Learning Operationalization (MLOps) Software market, by Application
SMEs
Large Enterprises
Core Chapters
Chapter One: Introduces the report scope of the report, executive summary of global and regional market size and CAGR for the history and forecast period (2018-2023, 2024-2029). 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 Two: 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 Three: 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 Four: Detailed analysis of AI & Machine Learning Operationalization (MLOps) Software companies’ competitive landscape, revenue, market share and ranking, latest development plan, merger, and acquisition information, etc.
Chapter Five: Provides profiles of key players, introducing the basic situation of the main companies in the market in detail, including product introduction, revenue, recent development, etc.
Chapter Six, Seven, Eight, Nine and Ten: North America, Europe, Asia Pacific, Latin America, Middle East & Africa, revenue by country.
Chapter Eleven: 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 Twelve: Research Finding/Conclusion
Index1 Market Overview of AI & Machine Learning Operationalization (MLOps) Software
1.1 AI & Machine Learning Operationalization (MLOps) Software Market Overview
1.1.1 AI & Machine Learning Operationalization (MLOps) Software Product Scope
1.1.2 AI & Machine Learning Operationalization (MLOps) Software Market Status and Outlook
1.2 Global AI & Machine Learning Operationalization (MLOps) Software Market Size Overview by Region 2018 VS 2022 VS 2029
1.3 Global AI & Machine Learning Operationalization (MLOps) Software Market Size by Region (2018-2029)
1.4 Global AI & Machine Learning Operationalization (MLOps) Software Historic Market Size by Region (2018-2023)
1.5 Global AI & Machine Learning Operationalization (MLOps) Software Market Size Forecast by Region (2024-2029)
1.6 Key Regions, AI & Machine Learning Operationalization (MLOps) Software Market Size (2018-2029)
1.6.1 North America AI & Machine Learning Operationalization (MLOps) Software Market Size (2018-2029)
1.6.2 Europe AI & Machine Learning Operationalization (MLOps) Software Market Size (2018-2029)
1.6.3 Asia-Pacific AI & Machine Learning Operationalization (MLOps) Software Market Size (2018-2029)
1.6.4 Latin America AI & Machine Learning Operationalization (MLOps) Software Market Size (2018-2029)
1.6.5 Middle East & Africa AI & Machine Learning Operationalization (MLOps) Software Market Size (2018-2029)
2 AI & Machine Learning Operationalization (MLOps) Software Market by Type
2.1 Introduction
2.1.1 Artificial Intelligence Platforms
2.1.2 Chatbots
2.1.3 Deep Learning Software
2.1.4 Machine Learning Software
2.2 Global AI & Machine Learning Operationalization (MLOps) Software Market Size by Type: 2018 VS 2022 VS 2029
2.2.1 Global AI & Machine Learning Operationalization (MLOps) Software Historic Market Size by Type (2018-2023)
2.2.2 Global AI & Machine Learning Operationalization (MLOps) Software Forecasted Market Size by Type (2024-2029)
2.3 Key Regions Market Size by Type
2.3.1 North America AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Type (2018-2029)
2.3.2 Europe AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Type (2018-2029)
2.3.3 Asia-Pacific AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Type (2018-2029)
2.3.4 Latin America AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Type (2018-2029)
2.3.5 Middle East and Africa AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Type (2018-2029)
3 AI & Machine Learning Operationalization (MLOps) Software Market Overview by Application
3.1 Introduction
3.1.1 SMEs
3.1.2 Large Enterprises
3.2 Global AI & Machine Learning Operationalization (MLOps) Software Market Size by Application: 2018 VS 2022 VS 2029
3.2.1 Global AI & Machine Learning Operationalization (MLOps) Software Historic Market Size by Application (2018-2023)
3.2.2 Global AI & Machine Learning Operationalization (MLOps) Software Forecasted Market Size by Application (2024-2029)
3.3 Key Regions Market Size by Application
3.3.1 North America AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Application (2018-2029)
3.3.2 Europe AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Application (2018-2029)
3.3.3 Asia-Pacific AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Application (2018-2029)
3.3.4 Latin America AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Application (2018-2029)
3.3.5 Middle East and Africa AI & Machine Learning Operationalization (MLOps) Software Revenue Breakdown by Application (2018-2029)
4 AI & Machine Learning Operationalization (MLOps) Software Competition Analysis by Players
4.1 Global AI & Machine Learning Operationalization (MLOps) Software Market Size by Players (2018-2023)
4.2 Global Top Players by Company Type (Tier 1, Tier 2 and Tier 3) & (based on the Revenue in AI & Machine Learning Operationalization (MLOps) Software as of 2022)
4.3 Date of Key Players Enter into AI & Machine Learning Operationalization (MLOps) Software Market
4.4 Global Top Players AI & Machine Learning Operationalization (MLOps) Software Headquarters and Area Served
4.5 Key Players AI & Machine Learning Operationalization (MLOps) Software Product Solution and Service
4.6 Competitive Status
4.6.1 AI & Machine Learning Operationalization (MLOps) Software Market Concentration Rate
4.6.2 Mergers & Acquisitions, Expansion Plans
5 Company (Top Players) Profiles
5.1 Google
5.1.1 Google Profile
5.1.2 Google Main Business
5.1.3 Google AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.1.4 Google AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.1.5 Google Recent Developments
5.2 Azure Machine Learning Studio
5.2.1 Azure Machine Learning Studio Profile
5.2.2 Azure Machine Learning Studio Main Business
5.2.3 Azure Machine Learning Studio AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.2.4 Azure Machine Learning Studio AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.2.5 Azure Machine Learning Studio Recent Developments
5.3 TensorFlow
5.3.1 TensorFlow Profile
5.3.2 TensorFlow Main Business
5.3.3 TensorFlow AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.3.4 TensorFlow AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.3.5 H2O.AI Recent Developments
5.4 H2O.AI
5.4.1 H2O.AI Profile
5.4.2 H2O.AI Main Business
5.4.3 H2O.AI AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.4.4 H2O.AI AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.4.5 H2O.AI Recent Developments
5.5 Cortana
5.5.1 Cortana Profile
5.5.2 Cortana Main Business
5.5.3 Cortana AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.5.4 Cortana AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.5.5 Cortana Recent Developments
5.6 IBM Watson
5.6.1 IBM Watson Profile
5.6.2 IBM Watson Main Business
5.6.3 IBM Watson AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.6.4 IBM Watson AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.6.5 IBM Watson Recent Developments
5.7 Salesforce Einstein
5.7.1 Salesforce Einstein Profile
5.7.2 Salesforce Einstein Main Business
5.7.3 Salesforce Einstein AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.7.4 Salesforce Einstein AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.7.5 Salesforce Einstein Recent Developments
5.8 Infosys Nia
5.8.1 Infosys Nia Profile
5.8.2 Infosys Nia Main Business
5.8.3 Infosys Nia AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.8.4 Infosys Nia AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.8.5 Infosys Nia Recent Developments
5.9 Amazon Alexa
5.9.1 Amazon Alexa Profile
5.9.2 Amazon Alexa Main Business
5.9.3 Amazon Alexa AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.9.4 Amazon Alexa AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.9.5 Amazon Alexa Recent Developments
5.10 SiQ
5.10.1 SiQ Profile
5.10.2 SiQ Main Business
5.10.3 SiQ AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.10.4 SiQ AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.10.5 SiQ Recent Developments
5.11 Robin
5.11.1 Robin Profile
5.11.2 Robin Main Business
5.11.3 Robin AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.11.4 Robin AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.11.5 Robin Recent Developments
5.12 Condeco
5.12.1 Condeco Profile
5.12.2 Condeco Main Business
5.12.3 Condeco AI & Machine Learning Operationalization (MLOps) Software Products, Services and Solutions
5.12.4 Condeco AI & Machine Learning Operationalization (MLOps) Software Revenue (US$ Million) & (2018-2023)
5.12.5 Condeco Recent Developments
6 North America
6.1 North America AI & Machine Learning Operationalization (MLOps) Software Market Size by Country (2018-2029)
6.2 United States
6.3 Canada
7 Europe
7.1 Europe AI & Machine Learning Operationalization (MLOps) Software Market Size by Country (2018-2029)
7.2 Germany
7.3 France
7.4 U.K.
7.5 Italy
7.6 Russia
7.7 Nordic Countries
7.8 Rest of Europe
8 Asia-Pacific
8.1 Asia-Pacific AI & Machine Learning Operationalization (MLOps) Software Market Size by Region (2018-2029)
8.2 China
8.3 Japan
8.4 South Korea
8.5 Southeast Asia
8.6 India
8.7 Australia
8.8 Rest of Asia-Pacific
9 Latin America
9.1 Latin America AI & Machine Learning Operationalization (MLOps) Software Market Size by Country (2018-2029)
9.2 Mexico
9.3 Brazil
9.4 Rest of Latin America
10 Middle East & Africa
10.1 Middle East & Africa AI & Machine Learning Operationalization (MLOps) Software Market Size by Country (2018-2029)
10.2 Turkey
10.3 Saudi Arabia
10.4 UAE
10.5 Rest of Middle East & Africa
11 AI & Machine Learning Operationalization (MLOps) Software Market Dynamics
11.1 AI & Machine Learning Operationalization (MLOps) Software Industry Trends
11.2 AI & Machine Learning Operationalization (MLOps) Software Market Drivers
11.3 AI & Machine Learning Operationalization (MLOps) Software Market Challenges
11.4 AI & Machine Learning Operationalization (MLOps) Software Market Restraints
12 Research Finding /Conclusion
13 Methodology and Data Source
13.1 Methodology/Research Approach
13.1.1 Research Programs/Design
13.1.2 Market Size Estimation
13.1.3 Market Breakdown and Data Triangulation
13.2 Data Source
13.2.1 Secondary Sources
13.2.2 Primary Sources
13.3 Disclaimer
13.4 Author List
Published By : QY Research
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