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Global MLOps Market to reach USD 17.26 billion by the end of 2030

Global MLOps Market Size Study & Forecast, by Component (Platform, Service), by Deployment (Cloud, On-premise), by Organization Size (SMEs, Large Enterprises), by Vertical (BFSI, Healthcare & Life Sciences, Retail & E-Commerce, IT & Telecom, Energy & Utilities, Government & Public Sector, Media & Entertainment, Others), and Regional Analysis, 2023-2030

Product Code: ICTICTI-57445896
Publish Date: 10-03-2024
Page: 200

Global MLOps Market is valued at approximately USD 1.19 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 39.7% during the forecast period 2023-2030. MLOps, or Machine Learning Operations, encompasses practices, tools, and methodologies aimed at streamlining the deployment, monitoring, and management of Machine Learning (ML) models in production environments. It integrates principles from DevOps to bridge the gap between data science and IT operations. Key components include version control for ML models and data, automated testing, CI/CD pipelines, model monitoring, and infrastructure management for scalable ML deployments. MLOps facilitates collaboration between data scientists, engineers, and operations teams, ensuring robust, reliable, and scalable ML models while upholding governance, compliance, and security standards. It plays a vital role in operationalizing ML, enabling organizations to derive value from their investments and drive innovation across various domains such as healthcare, finance, and e-commerce. The growing focus on the standardization of ML processes for effective teamwork, and improved efficiency due to increased monitorability, coupled with increased productivity and quicker AI implementation are the most prominent factors that are propelling the market demand across the globe.

In addition, the rapid shift towards cloud-based infrastructure and tools facilitates easier access to AI development and deployment for a wider range of users. According to Statista, the expenditure on cloud IT infrastructure accounts for nearly USD 94 billion in 2023, which is anticipated to surge to USD 133.7 billion by 2026. The expansion of public cloud infrastructure remains a significant catalyst for IT spending growth. Key players dominating the market landscape comprise Dell Technologies, HPE, Inspur, Lenovo, IBM, and Huawei. MLOps platforms leverage cloud capabilities to provide scalable, agile, and accessible solutions. Moreover, the rise in the use of machine learning in the financial sector, as well as the surge in demand for ML/AI-based projects among businesses presents various lucrative opportunities over the forecast years. However, the difficulty in managing various pipelines and the risk of raw data manipulation is hindering the market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global MLOps Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the region’s robust research and development competencies in Artificial Intelligence (AI), supported by well-established economies, research institutions, and leading AI firms. The growing investment in advanced technologies aimed at augmenting customer experiences and optimizing business operations is poised to create lucrative growth prospects across North America. Additionally, the region boasts sophisticated AI research and development capabilities, with substantial investments in AI-related technologies. Furthermore, North America has implemented policies conducive to fostering AI development. For instance, in December 2022, Allegro AI, an open-source company, announced significant growth milestones in user base, revenue, and collaborations, further underscoring the region’s commitment to advancing AI innovation. Whereas, Asia Pacific is expected to grow at the highest CAGR over the forecast years. The rapid growth of the cloud computing sector, along with key players like Amazon Web Services, Inc., Microsoft, and Google expanding their footprint are significantly propelling the market demand across the region. Cloud-based MLOps solutions are projected to witness substantial adoption in the region, as organizations integrate the scalability and flexibility of cloud infrastructure. Moreover, governments and enterprises across the APAC region are making significant investments in AI and machine learning, thereby fueling the demand for MLOps solutions capable of facilitating the development and deployment of machine learning models at scale.

Major market players included in this report are:
International Business Machines (IBM) Corporation
Microsoft Corporation
Google LLC
Amazon Web Services, Inc.
Hewlett Packard Enterprise Development LP
Neptune Labs, Inc.
DataRobot, Inc.
GAVS Technologies N.A., Inc.

Recent Developments in the Market:
Ø In April 2023, Canonical Ltd., a renowned computer software company, unveiled the release of Charmed Kubeflow, its machine learning operations toolkit, on Amazon Web Services Inc.’s cloud marketplace. This launch caters to businesses seeking to initiate and advance their machine learning and artificial intelligence endeavors effectively.
Ø In May 2022, GAVS Technologies announced its collaboration with NTT Ltd., marking the integration of the ZIF AIOps platform into NTT Ltd.’s Infrastructure Managed Services (IMS).
Ø In January 2021, Alteryx partnered with Snowflake to meet the increasing demand in the market. This partnership merges Alteryx’s data science and automation capabilities with Snowflake’s platform to provide shared customers with automated data pipelining, accelerated data processing, and enhanced analytics capabilities at scale.

Global MLOps Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Deployment, Organization Size, Vertical, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:

By Deployment:

By Organization Size:
Large Enterprises

By Vertical:
Healthcare & Life Sciences
Retail & E-Commerce
IT & Telecom
Energy & Utilities
Government & Public Sector
Media & Entertainment

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. MLOps Market, by Region, 2020-2030 (USD Billion)
1.2.2. MLOps Market, by Component, 2020-2030 (USD Billion)
1.2.3. MLOps Market, by Deployment, 2020-2030 (USD Billion)
1.2.4. MLOps Market, by Organization Size, 2020-2030 (USD Billion)
1.2.5. MLOps Market, by Vertical, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global MLOps Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global MLOps Market Dynamics
3.1. MLOps Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Rapid shift towards cloud-based infrastructure Rising focus on the standardization of ML processes for effective teamwork
3.1.2. Market Challenges Difficulty in managing various pipelines Risk of raw data manipulation
3.1.3. Market Opportunities Rise in use of machine learning in financial sector Surge in demand for ML/AI-based projects among businesses
Chapter 4. Global MLOps Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global MLOps Market, by Component
5.1. Market Snapshot
5.2. Global MLOps Market by Component, Performance – Potential Analysis
5.3. Global MLOps Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. MLOps Market, Sub Segment Analysis
5.4.1. Platform
5.4.2. Service
Chapter 6. Global MLOps Market, by Deployment
6.1. Market Snapshot
6.2. Global MLOps Market by Deployment, Performance – Potential Analysis
6.3. Global MLOps Market Estimates & Forecasts by Deployment 2020-2030 (USD Billion)
6.4. MLOps Market, Sub Segment Analysis
6.4.1. Cloud
6.4.2. On-premise
Chapter 7. Global MLOps Market, by Organization Size
7.1. Market Snapshot
7.2. Global MLOps Market by Organization Size, Performance – Potential Analysis
7.3. Global MLOps Market Estimates & Forecasts by Organization Size 2020-2030 (USD Billion)
7.4. MLOps Market, Sub Segment Analysis
7.4.1. SMEs
7.4.2. Large Enterprises
Chapter 8. MLOps Market, by Vertical
8.1. Market Snapshot
8.2. Global MLOps Market by Vertical, Performance – Potential Analysis
8.3. Global MLOps Market Estimates & Forecasts by Vertical 2020-2030 (USD Billion)
8.4. MLOps Market, Sub Segment Analysis
8.4.1. BFSI
8.4.2. Healthcare & Life Sciences
8.4.3. Retail & E-Commerce
8.4.4. IT & Telecom
8.4.5. Energy & Utilities
8.4.6. Government & Public Sector
8.4.7. Media & Entertainment
8.4.8. Others
Chapter 9. Global MLOps Market, Regional Analysis
9.1. Top Leading Countries
9.2. Top Emerging Countries
9.3. MLOps Market, Regional Market Snapshot
9.4. North America MLOps Market
9.4.1. U.S. MLOps Market Component breakdown estimates & forecasts, 2020-2030 Deployment breakdown estimates & forecasts, 2020-2030 Organization Size breakdown estimates & forecasts, 2020-2030 Vertical breakdown estimates & forecasts, 2020-2030
9.4.2. Canada MLOps Market
9.5. Europe MLOps Market Snapshot
9.5.1. U.K. MLOps Market
9.5.2. Germany MLOps Market
9.5.3. France MLOps Market
9.5.4. Spain MLOps Market
9.5.5. Italy MLOps Market
9.5.6. Rest of Europe MLOps Market
9.6. Asia-Pacific MLOps Market Snapshot
9.6.1. China MLOps Market
9.6.2. India MLOps Market
9.6.3. Japan MLOps Market
9.6.4. Australia MLOps Market
9.6.5. South Korea MLOps Market
9.6.6. Rest of Asia Pacific MLOps Market
9.7. Latin America MLOps Market Snapshot
9.7.1. Brazil MLOps Market
9.7.2. Mexico MLOps Market
9.8. Middle East & Africa MLOps Market
9.8.1. Saudi Arabia MLOps Market
9.8.2. South Africa MLOps Market
9.8.3. Rest of Middle East & Africa MLOps Market

Chapter 10. Competitive Intelligence
10.1. Key Company SWOT Analysis
10.1.1. Company 1
10.1.2. Company 2
10.1.3. Company 3
10.2. Top Market Strategies
10.3. Company Profiles
10.3.1. International Business Machines (IBM) Corporation Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
10.3.2. Microsoft Corporation
10.3.3. Google LLC
10.3.4. Amazon Web Services, Inc.
10.3.5. Hewlett Packard Enterprise Development LP
10.3.6. Neptune Labs, Inc.
10.3.7. DataRobot, Inc.
10.3.8. Dataiku.
10.3.9. ALTERYX, Inc.
10.3.10. GAVS Technologies N.A., Inc.
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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