The Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market should witness market growth of 40.3% CAGR during the forecast period (2022-2028).
Machine learning is one of the main growth drivers in the service sector, along with rising interest in cloud computing, AI, and cognitive computing. Increasing use of analytical solutions, the development of the artificial intelligence & cognitive computing industry, expanding application areas, and a lack of skilled personnel are some factors boosting demand for cloud-based solutions like cloud computing.
A model is just as trustworthy as the data it was trained on. This emerging realization prompts a fundamental transition from model-centric to data-centric machine learning solution development. The information is improved iteratively while the code is kept static in the data-centric approach.
Concerning the steel flaw identification and surface inspection use cases, this technique works well; enhancing the data has a more significant impact on model accuracy than upgrading the code. When training on small datasets where each item can significantly impact the eventual model's performance, 'good' data becomes even more crucial.
The startup ecosystems in China, India, the Philippines, and other developing nations in the region are vibrant and active thanks to a labour force that is getting better and more educated, which supports the growth of regional markets. Additionally, the Japanese government is implementing a variety of initiatives to promote AI and machine learning across the nation. This is in addition to a surge in the use of machine learning services, which is propelling the industry in Japan.
The China market dominated the Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Country in 2021, and would continue to be a dominant market till 2028; thereby, achieving a market value of $591.5 million by 2028. The Japan market is estimated to grow a CAGR of 39.5% during (2022-2028). Additionally, The India market would experience a CAGR of 41.2% during (2022-2028).
Based on Component, the market is segmented into Platform and Services. Based on Vertical, the market is segmented into BFSI, IT & ITeS, Manufacturing, Retail & Ecommerce, Government & Defense, Healthcare & Life Sciences, Telecom, Energy & Utilities, Travel & Tourism, and Others. Based on Organization size, the market is segmented into Large Enterprises and SMEs. Based on Deployment Mode, the market is segmented into Cloud and On-premise. Based on countries, the market is segmented into China, Japan, India, South Korea, Singapore, Malaysia, and Rest of Asia Pacific.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
Machine learning is one of the main growth drivers in the service sector, along with rising interest in cloud computing, AI, and cognitive computing. Increasing use of analytical solutions, the development of the artificial intelligence & cognitive computing industry, expanding application areas, and a lack of skilled personnel are some factors boosting demand for cloud-based solutions like cloud computing.
A model is just as trustworthy as the data it was trained on. This emerging realization prompts a fundamental transition from model-centric to data-centric machine learning solution development. The information is improved iteratively while the code is kept static in the data-centric approach.
Concerning the steel flaw identification and surface inspection use cases, this technique works well; enhancing the data has a more significant impact on model accuracy than upgrading the code. When training on small datasets where each item can significantly impact the eventual model's performance, 'good' data becomes even more crucial.
The startup ecosystems in China, India, the Philippines, and other developing nations in the region are vibrant and active thanks to a labour force that is getting better and more educated, which supports the growth of regional markets. Additionally, the Japanese government is implementing a variety of initiatives to promote AI and machine learning across the nation. This is in addition to a surge in the use of machine learning services, which is propelling the industry in Japan.
The China market dominated the Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Country in 2021, and would continue to be a dominant market till 2028; thereby, achieving a market value of $591.5 million by 2028. The Japan market is estimated to grow a CAGR of 39.5% during (2022-2028). Additionally, The India market would experience a CAGR of 41.2% during (2022-2028).
Based on Component, the market is segmented into Platform and Services. Based on Vertical, the market is segmented into BFSI, IT & ITeS, Manufacturing, Retail & Ecommerce, Government & Defense, Healthcare & Life Sciences, Telecom, Energy & Utilities, Travel & Tourism, and Others. Based on Organization size, the market is segmented into Large Enterprises and SMEs. Based on Deployment Mode, the market is segmented into Cloud and On-premise. Based on countries, the market is segmented into China, Japan, India, South Korea, Singapore, Malaysia, and Rest of Asia Pacific.
The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include Microsoft Corporation, Amazon Web Services, Inc. (Amazon.com, Inc.), Google LLC, IBM Corporation, Hewlett-Packard enterprise Company, Alteryx, Inc., Cloudera, Inc., DataRobot, Inc., Domino Data Lab, Inc., and H2O.ai, Inc.
Scope of the Study
By Component
- Platform
- Services
By Vertical
- BFSI
- IT & ITeS
- Manufacturing
- Retail & Ecommerce
- Government & Defense
- Healthcare & Life Sciences
- Telecom
- Energy & Utilities
- Travel & Tourism
- Others
By Organization size
- Large Enterprises
- SMEs
By Deployment Mode
- Cloud
- On-premise
By Country
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
Key Market Players
List of Companies Profiled in the Report:
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Google LLC
- IBM Corporation
- Hewlett-Packard enterprise Company
- Alteryx, Inc.
- Cloudera, Inc.
- DataRobot, Inc.
- Domino Data Lab, Inc.
- H2O.ai, Inc.
Unique Offerings
- Exhaustive coverage
- The highest number of market tables and figures
- Subscription-based model available
- Guaranteed best price
- Assured post sales research support with 10% customization free
Table of Contents
Chapter 1. Market Scope & Methodology1.1 Market Definition
1.2 Objectives
1.3 Market Scope
1.4 Segmentation
1.4.1 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market, by Component
1.4.2 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market, by Vertical
1.4.3 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market, by Organization size
1.4.4 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market, by Deployment Mode
1.4.5 Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market, by Country
1.5 Methodology for the research
Chapter 2. Market Overview
2.1 Introduction
2.1.1 Overview
2.1.1.1 Market Composition and Scenario
2.2 Key Factors Impacting the Market
2.2.1 Market Drivers
2.2.2 Market Restraints
Chapter 3. Competition Analysis - Global
3.1 Cardinal Matrix
3.2 Recent Industry Wide Strategic Developments
3.2.1 Partnerships, Collaborations and Agreements
3.2.2 Product Launches and Product Expansions
3.2.3 Acquisition and Mergers
3.3 Top Winning Strategies
3.3.1 Key Leading Strategies: Percentage Distribution (2018-2022)
3.3.2 Key Strategic Move: (Product Launches and Product Expansions : 2018, Nov - 2022, Dec) Leading Players
Chapter 4. Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Component
4.1 Asia Pacific Platform Market by Country
4.2 Asia Pacific Services Market by Country
Chapter 5. Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Vertical
5.1 Asia Pacific BFSI Market by Country
5.2 Asia Pacific IT & ITeS Market by Country
5.3 Asia Pacific Manufacturing Market by Country
5.4 Asia Pacific Retail & Ecommerce Market by Country
5.5 Asia Pacific Government & Defense Market by Country
5.6 Asia Pacific Healthcare & Life Sciences Market by Country
5.7 Asia Pacific Telecom Market by Country
5.8 Asia Pacific Energy & Utilities Market by Country
5.9 Asia Pacific Travel & Tourism Market by Country
5.1 Asia Pacific Other Vertical Market by Country
Chapter 6. Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Organization size
6.1 Asia Pacific Large Enterprises Market by Country
6.2 Asia Pacific SMEs Market by Country
Chapter 7. Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
7.1 Asia Pacific Cloud Market by Country
7.2 Asia Pacific On-premise Market by Country
Chapter 8. Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Country
8.1 China Machine Learning Model Operationalization Management (MLOps) Market
8.1.1 China Machine Learning Model Operationalization Management (MLOps) Market by Component
8.1.2 China Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.1.3 China Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.1.4 China Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.2 Japan Machine Learning Model Operationalization Management (MLOps) Market
8.2.1 Japan Machine Learning Model Operationalization Management (MLOps) Market by Component
8.2.2 Japan Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.2.3 Japan Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.2.4 Japan Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.3 India Machine Learning Model Operationalization Management (MLOps) Market
8.3.1 India Machine Learning Model Operationalization Management (MLOps) Market by Component
8.3.2 India Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.3.3 India Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.3.4 India Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.4 South Korea Machine Learning Model Operationalization Management (MLOps) Market
8.4.1 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Component
8.4.2 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.4.3 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.4.4 South Korea Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.5 Singapore Machine Learning Model Operationalization Management (MLOps) Market
8.5.1 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Component
8.5.2 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.5.3 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.5.4 Singapore Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.6 Malaysia Machine Learning Model Operationalization Management (MLOps) Market
8.6.1 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Component
8.6.2 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.6.3 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.6.4 Malaysia Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
8.7 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market
8.7.1 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Component
8.7.2 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Vertical
8.7.3 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Organization size
8.7.4 Rest of Asia Pacific Machine Learning Model Operationalization Management (MLOps) Market by Deployment Mode
Chapter 9. Company Profiles
9.1 Microsoft Corporation
9.1.1 Company Overview
9.1.2 Financial Analysis
9.1.4 Research & Development Expenses
9.1.5 Recent strategies and developments:
9.1.5.1 Partnerships, Collaborations, and Agreements:
9.1.5.2 Product Launches and Product Expansions:
9.1.6 SWOT Analysis
9.2 Amazon Web Services, Inc. (Amazon.com, Inc.)
9.2.1 Company Overview
9.2.2 Financial Analysis
9.2.3 Segmental Analysis
9.2.4 Recent strategies and developments:
9.2.4.1 Partnerships, Collaborations, and Agreements:
9.2.4.2 Product Launches and Product Expansions:
9.2.5 SWOT Analysis
9.3 Google LLC
9.3.1 Company Overview
9.3.2 Financial Analysis
9.3.3 Segmental and Regional Analysis
9.3.4 Research & Development Expense
9.3.5 Recent strategies and developments:
9.3.5.1 Product Launches and Product Expansions:
9.3.6 SWOT Analysis
9.4 IBM Corporation
9.4.1 Company Overview
9.4.2 Financial Analysis
9.4.3 Regional & Segmental Analysis
9.4.4 Research & Development Expenses
9.4.5 Recent strategies and developments:
9.4.5.1 Acquisition and Mergers:
9.4.1 SWOT Analysis
9.5 Hewlett Packard Enterprise Company
9.5.1 Company Overview
9.5.2 Financial Analysis
9.5.3 Segmental and Regional Analysis
9.5.4 Research & Development Expense
9.5.5 Recent strategies and developments:
9.5.5.1 Product Launches and Product Expansions:
9.5.5.2 Acquisition and Mergers:
9.6 Alteryx, Inc.
9.6.1 Company Overview
9.6.2 Financial Analysis
9.6.3 Regional Analysis
9.6.4 Research & Development Expense
9.6.5 Recent strategies and developments:
9.6.5.1 Product Launches and Product Expansions:
9.7 Cloudera, Inc.
9.7.1 Company Overview
9.7.2 Financial Analysis
9.7.3 Segmental Analysis
9.7.4 Research & Development Expense
9.8 DataRobot, Inc.
9.8.1 Company Overview
9.8.2 Recent strategies and developments:
9.8.2.1 Acquisition and Mergers:
9.9 Domino Data Lab, Inc.
9.9.1 Company Overview
9.9.2 Recent strategies and developments:
9.9.2.1 Partnerships, Collaborations, and Agreements:
9.9.2.2 Product Launches and Product Expansions:
9.10. H2O.ai, Inc.
9.10.1 Company Overview
9.10.2 Recent strategies and developments:
9.10.2.1 Partnerships, Collaborations, and Agreements:
9.10.2.2 Product Launches and Product Expansions:
Companies Mentioned
- Microsoft Corporation
- Amazon Web Services, Inc. (Amazon.com, Inc.)
- Google LLC
- IBM Corporation
- Hewlett-Packard enterprise Company
- Alteryx, Inc.
- Cloudera, Inc.
- DataRobot, Inc.
- Domino Data Lab, Inc.
- H2O.ai, Inc.
Methodology
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