The Cloud Machine Learning Operations (MLOps) Market Size is valued at USD 196.5 million in 2023 and is predicted to reach USD 3,156.0 million by the year 2031 at a 42.3% CAGR during the forecast period for 2024-2031.
Machine Learning Operations, often known as MLOps, is a term that describes the operational procedures and practices that are used in order to manage and operationalize machine learning models within a production setting. In order to guarantee that the deployment, monitoring, and upkeep of machine learning models go off without a hitch, the integration of a number of different processes, tools, and approaches is required. Manual data reprocessing and collection are inefficient processes that also have the potential to give outcomes that are not adequate. All of the ML model processes may be automated with the assistance of MLOps. The collecting of data, the creation of models, testing, retraining, and deployment are all included in this. By reducing mistake rates and saving time, MLOps are beneficial to businesses. In order to implement machine learning models throughout the whole organization, a collaborative effort must be made between IT and business executives, as well as data scientists and engineers.
Nevertheless, raw data is employed to produce predictions and extract final outcomes when a model is implemented. Consequently, it becomes difficult to ascertain the model's accuracy and to conduct ongoing evaluations. The procedure is delayed and ineffectual for ongoing retraining duties when the new data is manually labeled. The decision to employ unsupervised learning instead of supervised learning or to use the trained model to label the fresh data is contingent upon the issue and the task's objectives.
The Cloud Machine Learning Operations (MLOps) market is segmented on the basis of Type and application. Based on Type, the market is segmented as Platform, and Services. By application segment, the market is further segmented into BFSI, Healthcare, Retail, Manufacturing, Public Sector, and Others.
The Platform category is expected to hold a major share in the global Cloud Machine Learning Operations (MLOps) market in 2023. The Platform segment, which encompasses a variety of comprehensive tools and frameworks, was instrumental in enabling organizations spanning a variety of industries to execute end-to-end machine learning workflows. The Platform segment's preeminent position was propelled by the rise in demand for comprehensive MLOps alternatives that seamlessly integrate with existing infrastructures. A collection of technologies that are intended to simplify the deployment, monitoring, and administration of machine learning models is included in the Platform component of MLOps. The ascendance of this segment can be ascribed to the growing number of automated machine learning (AutoML) platforms, which enable organizations to capitalize on machine learning (ML) capabilities without the necessity for extensive expertise.
The Healthcare segment is projected to grow rapidly in the global Cloud Machine Learning Operations (MLOps) market. The primary factor driving this accelerated growth is the growing adoption of machine learning as well as artificial intelligence (AI) to improve patient outcomes and simplify healthcare processes. AI-powered tools are extensively used for predictive modelling, personalized treatment recommendations, and image analysis. MLOps solutions enable the deployment and administration of these tools at scale, thereby guaranteeing adherence to rigorous data privacy regulations.
The North America Cloud Machine Learning Operations (MLOps) market is expected to register the major market share in terms of revenue in the near future. The area represents the pinnacle of technical developments in machine learning across several industries, including banking, retail, automotive, and healthcare, among others. Additionally, several pharmaceutical and property and casualty insurance entities engage in machine learning technology for business innovation. The Asia-Pacific (APAC) region is seeing significant expansion in the MLOps market, propelled by digital transformation efforts in nations such as China, Japan, India, and South Korea. These nations are achieving considerable advancements in artificial intelligence and machine learning, supported by governmental backing and investments. The expanding IT industry, together with an increase in data-centric enterprises, is driving the need for MLOps solutions in the area.
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Report Attribute |
Specifications |
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Market Size Value In 2023 |
USD 196.5 Mn |
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Revenue Forecast In 2031 |
USD 3,156.0 Mn |
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Growth Rate CAGR |
CAGR of 42.3% from 2024 to 2031 |
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Quantitative Units |
Representation of revenue in US$ Mn and CAGR from 2024 to 2031 |
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Historic Year |
2019 to 2023 |
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Forecast Year |
2024-2031 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
IBM, DataRobot, SAS, Microsoft, Amazon, Google, Dataiku, Databricks, HPE, Lguazio, ClearML, Modzy, Comet, Cloudera, Paperpace, Valohai. |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Cloud Machine Learning Operations (MLOps) Market Snapshot
Chapter 4. Global Cloud Machine Learning Operations (MLOps) Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Porter's Five Forces Analysis
4.7. Incremental Opportunity Analysis (US$ MN), 2024-2031
4.8. Global Cloud Machine Learning Operations (MLOps) Market Penetration & Growth Prospect Mapping (US$ Mn), 2023-2031
4.9. Competitive Landscape & Market Share Analysis, By Key Player (2023)
4.10. Use/impact of AI on Cloud Machine Learning Operations (MLOps) Industry Trends
Chapter 5. Cloud Machine Learning Operations (MLOps) Market Segmentation 1: By Type, Estimates & Trend Analysis
5.1. Market Share by Type, 2023 & 2031
5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following Type:
5.2.1. Platform
5.2.2. Services
Chapter 6. Cloud Machine Learning Operations (MLOps) Market Segmentation 2: By Application, Estimates & Trend Analysis
6.1. Market Share by Application, 2023 & 2031
6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2019 to 2031 for the following Application:
6.2.1. BFSI
6.2.2. Healthcare
6.2.3. Retail
6.2.4. Manufacturing
6.2.5. Public Sector
6.2.6. Others
Chapter 7. Cloud Machine Learning Operations (MLOps) Market Segmentation 3: Regional Estimates & Trend Analysis
7.1. Global Cloud Machine Learning Operations (MLOps) Market, Regional Snapshot 2023 & 2031
7.2. North America
7.2.1. North America Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
7.2.1.1. US
7.2.1.2. Canada
7.2.2. North America Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
7.2.3. North America Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
7.3. Europe
7.3.1. Europe Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
7.3.1.1. Germany
7.3.1.2. U.K.
7.3.1.3. France
7.3.1.4. Italy
7.3.1.5. Spain
7.3.1.6. Rest of Europe
7.3.2. Europe Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
7.3.3. Europe Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
7.4. Asia Pacific
7.4.1. Asia Pacific Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
7.4.1.1. India
7.4.1.2. China
7.4.1.3. Japan
7.4.1.4. Australia
7.4.1.5. South Korea
7.4.1.6. Hong Kong
7.4.1.7. Southeast Asia
7.4.1.8. Rest of Asia Pacific
7.4.2. Asia Pacific Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
7.4.3. Asia Pacific Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts By Application, 2024-2031
7.5. Latin America
7.5.1. Latin America Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Country, 2024-2031
7.5.1.1. Brazil
7.5.1.2. Mexico
7.5.1.3. Rest of Latin America
7.5.2. Latin America Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
7.5.3. Latin America Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
7.6. Middle East & Africa
7.6.1. Middle East & Africa Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031
7.6.1.1. GCC Countries
7.6.1.2. Israel
7.6.1.3. South Africa
7.6.1.4. Rest of Middle East and Africa
7.6.2. Middle East & Africa Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
7.6.3. Middle East & Africa Cloud Machine Learning Operations (MLOps) Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
Chapter 8. Competitive Landscape
8.1. Major Mergers and Acquisitions/Strategic Alliances
8.2. Company Profiles
8.2.1. International Business Machines Corporation (IBM)
8.2.1.1. Business Overview
8.2.1.2. Key Product/Service Offerings
8.2.1.3. Financial Performance
8.2.1.4. Geographical Presence
8.2.1.5. Recent Developments with Business Strategy
8.2.2. DataRobot, Inc.
8.2.3. Microsoft Corporation
8.2.4. Amazon.com, Inc.
8.2.5. Google LLC
8.2.6. Dataiku Inc.
8.2.7. Databricks, Inc.
8.2.8. Hewlett Packard Enterprise Development LP (HPE)
8.2.9. Iguazio Ltd.
8.2.10. ClearML, Inc.
8.2.11. Modzy LLC
8.2.12. Comet ML, Inc.
8.2.13. Cloudera, Inc.
8.2.14. Paperpace, Inc.
8.2.15. Valohai Oy
8.2.16. Other Prominent Players
Cloud Machine Learning Operations (MLOps) Market By Type-
Cloud Machine Learning Operations (MLOps) Market By Application-
Cloud Machine Learning Operations (MLOps) Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.
Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.
Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.
Secondary research
The secondary research sources that are typically mentioned to include, but are not limited to:
The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista
Primary Research:
Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies
The contributors who typically take part in such a course include, but are not limited to:
Data Modeling and Analysis:
In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.
The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.
