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The MLOps Market Size was valued at USD 1.3 Billion in 2023. It is expected to grow to USD 29.6 Billion by 2032 and grow at a CAGR of 41.6% over the forecast period of 2024-2032.
The ability to integrate MLOps with existing DevOps is becoming more of a standard practice among organizations aiming to enhance the effectiveness of their machine learning work. By adopting DevOps practices, organizations can improve the agility and reliability of their machine learning deployments. This integration includes several practices that make MLOps a part of DevOps, such as the use of version control to provide insights into the changes in machine learning models and data and allow for the reversal of changes. Next, the use of CI/CD pipelines enables automated testing and deployment processes and quick iteration as well as minimal deployment issues or downtime.
The process of rolling out updates ensures that models are always tested against performance metrics, and deploying them is their final validation step. Last, monitoring models that are already in production reveals such issues as data drift or model degradation and allows for the maintenance of optimum performance. By combining MLOps with DevOps, organizations can create a positive, encouraging, and innovative culture that allows them to deliver more effective and coherent machine learning solutions.
The U.S. Bureau of Labor Statistics projects that employment in computer and information technology occupations, which includes AI and ML fields, will grow 15% from 2021 to 2032, much faster than the average for all occupations, highlighting the increasing relevance of these technologies in business.
With the rising realization of the transformative effects of artificial intelligence and machine learning on enhancing business innovation and operational efficiency, there is an accelerating demand for MLOps solutions. The latter is indispensable for streamlining the deployment, monitoring, and management of ML models at scale. Since businesses aspire to derive competitive advantage from data-driven insights, one can no longer ignore the complexity of effectively managing multiple models in production. MLOps is a structured framework that harnesses best practices from development and operations, implying that the models are not only deployed fast but also constantly monitored whether they effectively and reliably perform. According to a 2022 report by the National Institute of Standards and Technology (NIST), 61% of U.S. businesses reported adopting AI technologies, with many indicating that these technologies are critical for enhancing efficiency and operational processes.
Drivers
Increasing adoption of machine learning and artificial intelligence.
The increase in internet and digital adoption around the world has a beneficial effect on market expansion.
Growing demand for cloud-based MLOps solutions.
The increasing demand for cloud-based MLOps solutions that can help companies organize their machine learning operations. Due to the growing adoption of AI and ML technologies among various organizations, the issue of managing multiple ML models has become especially relevant. Cloud-based MLOps solutions provide companies with a seamless way to address this problem. Firstly, they ensure high efficiency and scalability of the infrastructure. Thus, based on the volume of uploaded data, models, and required computational capacity, organizations dynamically manage resources implemented in the cloud, gaining considerable benefits in terms of managing the workload. Secondly, by running all necessary tools in the cloud, the platforms enhance collaboration between data scientists, engineers, and business specialists.
It is because of the nature of cloud environments, knowledge workers can access the necessary tools from any location to run their experiments. The sphere of machine learning, as well as business in general, is characterized by the demand for high speed, hence the ability to quickly conduct experiments and adjust models. Finally, cloud providers can ensure the highest level of security and design robust compliance frameworks. In conclusion, the adoption of AI and ML will only increase in the upcoming years, and the demand for cloud-based MLOps platforms is expected to grow as well.
A report from the National Institute of Standards and Technology (NIST) noted that over 70% of organizations are integrating AI into their cloud-based platforms, reflecting the growing reliance on cloud environments for AI and machine learning operations.
Restraint
Complexity of MLOps
Lack of awareness about MLOps among many organizations.
One of the most substantial constraints to machine learning practices and technologies is the lack of awareness about MLOps in many organizations. Despite the increasing recognition of artificial intelligence and machine learning as potent tools for helping companies expand and improve their operations MAI21, many companies, particularly small and medium enterprises, do not have adequate reasons to learn about the benefits of MLOps, which can result from a variety of factors. Some of these include a scarcity of educational opportunities, a lack of experience in data science, or the absence of a compelling case demonstrating the advantages of MLOps. Accordingly, decision-makers might undervalue the role such an operation framework may play in the process of deploying, monitoring, and managing ML models.
Opportunity
The rise of edge computing
Demand for MLOps tools and solutions presents opportunities for companies to develop and provide innovative MLOps tools and platforms.
Edge computing is a new paradigm for computing that brings computation and storage closer to the data source. This is creating new opportunities for MLOps, as it allows organizations to deploy and run ML models at the edge. The advent of DNA microarray technology made possible screening a relatively large number of SNPs simultaneously. This technology is crucial in population screening for susceptibility to multifactorial diseases. Technology is very useful in the diagnosis of hemoglobinopathies simultaneously. DNA sequences can be tested on one slide and multiple people evaluated at a relatively low cost. Understanding various single nucleotide polymorphisms present in each of the multiple genes evaluated would not have been possible a few years ago. DNA microarray technology has enabled a revolution in the genetic testing of a variety of diseases.
By Component
The platform segment held the largest market share of over 64% as of 2023. MLOps platforms are offered as comprehensive tools with a variety of integrated services, covering the whole range of tasks necessary to support this life cycle. In this way, data preparation, model training, deployment, and monitoring can all be carried out with such solutions, including such vital stages as model training and data preparation. They help enhance the collaboration of data scientists, engineers, IT departments, and others, ensuring that all of the parties involved experience simplified and more facilitated operations. More importantly, this approach contributes to a substantial decrease in time needed to start running machine learning applications or models, and, providing such features as version control, automated tests, and CI/CD, platforms also contribute to the reliability, scalability, and stability of the solutions offered.
By Deployment
The cloud segment held the largest market share around 42% in 2023. Cloud-based solutions are widespread nowadays due to the scalability and flexibility they provide. This is a feature an organization can manage and deploy machine learning solutions without the necessity of having a vast on-the-ground facility. Speaking of scalability, for example, it is important for enterprises with a vast workload and huge datasets as cloud solutions smoothly allocate necessary resources and maximize them to meet the demand. In addition, companies that supply clouds might offer a range of tools stretching from data storage to training and model facilitating its deployment, thus, appealing to businesses due to the wide options available. In addition, the cloud is a proper environment for collaboration since it is easily accessible to professionals from different parts of the world
By End-Use
IT & Telecom segment held the largest market share around 30% in 2023. IT and telecom are dominant in their specific features that make the demand for solutions and products they have very high. It can mainly be explained by the intensive development of such technologies as cloud computing, artificial intelligence, the Internet of Things, etc., that require robust IT and telecom solutions. Companies operating in the segment have to look for new and more efficient ways of working, seek to cut costs and improve the quality of products and services. The current trend for digital communication and the across-the-board introduction of 5G encourages those companies to create new products and services, hence, leading to the further development of their MLOps.
North America dominated the market in 2023, accounting for over 44% of the worldwide revenue. Due to Al's strong R&D capacities in developed economies, research institutions, and numerous top Al firms situated in this area. It is projected that North America will see profitable growth prospects as a result of the rising investment in cutting-edge technologies to improve customer experience and business processes. Additionally, over the past few years, the region has made significant investments in technology related to aluminumw2 and has strong R&D skills in the field. To assist the advancement of the field, they have also implemented policies. For instance, open-source business Allegro AI announced in December 2022 that it had passed a significant growth milestone, setting new benchmarks in user base, revenue, and collaborations. The company also announced opening its first office in the U.S. to meet the high demand for its platform
Asia Pacific is expected to have the highest CAGR over the forecast period. Cloud computing in the area is expanding quickly, with major businesses like Amazon Web Services, Inc., Microsoft, and Google increasing their presence there. As enterprises take use of cloud infrastructure's scalability and flexibility, cloud-based MLOps solutions are anticipated to experience increasing adoption in the region. Additionally, governments and companies in the APAC region are making significant investments in AI and machine learning. This investment increases demand for MLOps solutions, which enable businesses to rapidly build and use machine learning models.
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Key Players in MLOps Market
IBM Corporation (IBM Watson Studio)
GAVS Technologies (MLOps.ai)
Amazon Web Services, Inc. (Amazon SageMaker)
Databricks, Inc. (Databricks Unified Analytics Platform)
DataRobot, Inc. (DataRobot MLOps)
Microsoft Corporation (Azure Machine Learning)
Cloudera, Inc. (Cloudera Machine Learning)
Akira AI (Akira AI Platform)
Alteryx (Alteryx Designer)
Google LLC (Google AI Platform)
H2O.ai (H2O Driverless AI)
NVIDIA Corporation (NVIDIA Triton Inference Server)
Tecton (Tecton Feature Store)
Paperspace (Paperspace Gradient)
Kubeflow (Kubeflow Pipelines)
MLflow (MLflow Tracking)
Seldon Technologies (Seldon Core)
ClearML (ClearML Platform)
Weight & Biases (WandB)
Neptune.ai (Neptune.ai)
Facebook (Meta Platforms, Inc.)
JPMorgan Chase
Goldman Sachs
Mayo Clinic
Philips Healthcare
Walmart
Amazon
AT&T
Verizon
Tesla
Recent Development:
In April 2023, Canonical Ltd., launched Charmed Kubeflow, its machine learning operations toolkit, on Amazon Web Services Inc.’s cloud marketplace. The new launch is planned for businesses looking to kickstart their ML and AI initiatives.
In 2023, AWS introduced new features in Amazon SageMaker, including SageMaker Canvas for visual data science and SageMaker Model Registry, enabling better management of machine learning models.
In 2023, IBM launched an upgraded version of its Watson Studio platform, enhancing collaboration tools and integrating more robust MLOps features for improved model deployment and monitoring.
| Report Attributes | Details |
| Market Size in 2023 | US$ 1.3 Bilion |
| Market Size by 2032 | US$ 29.6 Billion |
| CAGR | CAGR of 41.6% From 2024 to 2032 |
| Base Year | 2023 |
| Forecast Period | 2024-2032 |
| Historical Data | 2020-2022 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Component (Platform, Service) • By Deployment Mode (On-Premise, Cloud) • By Organization Size (Large Enterprises, Small and Medium-sized Enterprises) • By End-Use Vertical (IT, Telecom Services, Government, BFSI, Retail, Consumer Goods, Transportation, Others) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe [Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
| Company Profiles | IBM Corporation, GAVS Technologies, Amazon Web Services, Inc., Databricks, Inc., DataRobot, Inc., Microsoft Corporation, Cloudera, Inc., Akira AI, Alteryx, Google LLC |
| Key Drivers | • Increasing adoption of machine learning and artificial intelligence. • The increase in internet and digital adoption around the world has a beneficial effect on market expansion. • Growing demand for cloud-based MLOps solutions. |
| Market Restraints | • Complexity of MLOps • There is still a lack of awareness about MLOps among many organizations. |
Ans. The Compound Annual Growth rate for MLOps Market over the forecast period is 43.16%.
Ans. USD 17,969.20 Million is the Company's projected MLOps Market size by 2032.
Ans. MLOps, short for Machine Learning Operations, is an approach to managing the lifecycle of machine learning models. It encompasses various stages, including data gathering, model development, deployment, monitoring, and governance.
Ans. MLOps is crucial in modern businesses because it offers several benefits, including faster go-to-market times, lower operational costs, improved decision-making, and more effective automation.
Ans. MLOps draws inspiration from DevOps practices for software development. It brings together diverse teams in an organization to accelerate the development and deployment of machine learning models.
1. Introduction
1.1 Market Definition
1.2 Scope (Inclusion and Exclusions)
1.3 Research Assumptions
2. Executive Summary
2.1 Market Overview
2.2 Regional Synopsis
2.3 Competitive Summary
3. Research Methodology
3.1 Top-Down Component
3.2 Bottom-up Component
3.3. Data Validation
3.4 Primary Interviews
4. Market Dynamics Impact Analysis
4.1 Market Driving Factors Analysis
4.1.1 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.1.4 Challenges
4.2 PESTLE Analysis
4.3 Porter’s Five Forces Model
5. Statistical Insights and Trends Reporting
5.1 Adoption Rates of Emerging Technologies
5.2 Network Infrastructure Expansion, by Region
5.3 Cybersecurity Incidents, by Region (2020-2023)
5.4 Cloud Services Usage, by Region
6. Competitive Landscape
6.1 List of Major Companies, By Region
6.2 Market Share Analysis, By Region
6.3 Type Benchmarking
6.3.1 Type specifications and features
6.3.2 Pricing
6.4 Strategic Initiatives
6.4.1 Marketing and promotional activities
6.4.2 Distribution and supply chain strategies
6.4.3 Expansion plans and new Age Cohort launches
6.4.4 Strategic partnerships and collaborations
6.5 Technological Advancements
6.6 Market Positioning and Branding
7. MLOps Market Segmentation, By Component
7.1 Chapter Overview
7.2 Platform
7.2.1 Platform Market Trends Analysis (2020-2032)
7.2.2 Platform Market Size Estimates and Forecasts to 2032 (USD Billion)
7.3 Service
7.3.1 Service Market Trends Analysis (2020-2032)
7.3.2 Service Market Size Estimates and Forecasts to 2032 (USD Billion)
8. MLOps Market Segmentation, By Deployment
8.1 Chapter Overview
8.2 On-Premise
8.2.1 On-Premise Market Trends Analysis (2020-2032)
8.2.2 On-Premise Market Size Estimates and Forecasts to 2032 (USD Billion)
8.3 Cloud
8.3.1 Cloud Market Trends Analysis (2020-2032)
8.3.2 Cloud Market Size Estimates and Forecasts to 2032 (USD Billion)
8.4 Hybrid
8.4.1 Hybrid Market Trends Analysis (2020-2032)
8.4.2 Hybrid Market Size Estimates and Forecasts to 2032 (USD Billion)
9. MLOps Market Segmentation, By Organization Size
9.1 Chapter Overview
9.2 Large Enterprise
9.2.1 Large Enterprise Market Trends Analysis (2020-2032)
9.2.2 Large Enterprise Market Size Estimates and Forecasts to 2032 (USD Billion)
9.3 SMEs
9.3.1 SMEs Market Trends Analysis (2020-2032)
9.3.2 SMEs Market Size Estimates and Forecasts to 2032 (USD Billion)
10. MLOps Market Segmentation, By End-Use
10.1 Chapter Overview
10.2 BFSI
10.2.1 BFSI Market Trends Analysis (2020-2032)
10.2.2 BFSI Market Size Estimates and Forecasts to 2032 (USD Billion)
10.3 Healthcare & Life Sciences
10.3.1 Healthcare & Life Sciences Market Trends Analysis (2020-2032)
10.3.2 Healthcare & Life Sciences Market Size Estimates and Forecasts to 2032 (USD Billion)
10.4 Retail & E-Commerce
10.4.1 Retail & E-Commerce Market Trends Analysis (2020-2032)
10.4.2 Retail & E-Commerce Market Size Estimates and Forecasts to 2032 (USD Billion)
10.5 IT & Telecom
10.5.1 IT & Telecom Market Trends Analysis (2020-2032)
10.5.2 IT & Telecom Market Size Estimates and Forecasts to 2032 (USD Billion)
10.6 Energy & Utilities
10.6.1 Energy & Utilities Market Trends Analysis (2020-2032)
10.6.2 Energy & Utilities Market Size Estimates and Forecasts to 2032 (USD Billion)
10.7 Government & Public Sector
10.7.1 Government & Public Sector Market Trends Analysis (2020-2032)
10.7.2 Government & Public Sector Market Size Estimates and Forecasts to 2032 (USD Billion)
11. Regional Analysis
11.1 Chapter Overview
11.2 North America
11.2.1 Trends Analysis
11.2.2 North America MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.2.3 North America MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.2.4 North America MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.2.5 North America MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.2.6 North America MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.2.7 USA
11.2.7.1 USA MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.2.7.2 USA MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.2.7.3 USA MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.2.7.4 USA MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.2.8 Canada
11.2.8.1 Canada MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.2.8.2 Canada MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.2.8.3 Canada MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.2.8.4 Canada MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.2.9 Mexico
11.2.9.1 Mexico MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.2.9.2 Mexico MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.2.9.3 Mexico MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.2.9.4 Mexico MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3 Europe
11.3.1 Eastern Europe
11.3.1.1 Trends Analysis
11.3.1.2 Eastern Europe MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.3.1.3 Eastern Europe MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.1.4 Eastern Europe MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.1.5 Eastern Europe MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.1.6 Eastern Europe MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.1.7 Poland
11.3.1.7.1 Poland MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.1.7.2 Poland MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.1.7.3 Poland MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.1.7.4 Poland MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.1.8 Romania
11.3.1.8.1 Romania MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.1.8.2 Romania MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.1.8.3 Romania MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.1.8.4 Romania MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.1.9 Hungary
11.3.1.9.1 Hungary MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.1.9.2 Hungary MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.1.9.3 Hungary MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.1.9.4 Hungary MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.1.10 Turkey
11.3.1.10.1 Turke MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.1.10.2 Turkey MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.1.10.3 Turkey MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.1.10.4 Turkey MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.1.11 Rest of Eastern Europe
11.3.1.11.1 Rest of Eastern Europe MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.1.11.2 Rest of Eastern Europe MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.1.11.3 Rest of Eastern Europe MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.1.11.4 Rest of Eastern Europe MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2 Western Europe
11.3.2.1 Trends Analysis
11.3.2.2 Western Europe MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.3.2.3 Western Europe MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.4 Western Europe MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.5 Western Europe MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.6 Western Europe MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.7 Germany
11.3.2.7.1 Germany MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.7.2 Germany MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.7.3 Germany MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.7.4 Germany MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.8 France
11.3.2.8.1 France MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.8.2 France MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.8.3 France MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.8.4 France MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.9 UK
11.3.2.9.1 UK MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.9.2 UK MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.9.3 UK MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.9.4 UK MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.10 Italy
11.3.2.10.1 Italy MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.10.2 Italy MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.10.3 Italy MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.10.4 Italy MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.11 Spain
11.3.2.11.1 Spain MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.11.2 Spain MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.11.3 Spain MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.11.4 Spain MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.12 Netherlands
11.3.2.12.1 Netherlands MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.12.2 Netherlands MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.12.3 Netherlands MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.12.4 Netherlands MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.13 Switzerland
11.3.2.13.1 Switzerland MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.13.2 Switzerland MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.13.3 Switzerland MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.13.4 Switzerland MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.14 Austria
11.3.2.14.1 Austria MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.14.2 Austria MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.14.3 Austria MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.14.4 Austria MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.3.2.15 Rest of Western Europe
11.3.2.15.1 Rest of Western Europe MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.3.2.15.2 Rest of Western Europe MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.3.2.15.3 Rest of Western Europe MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.3.2.15.4 Rest of Western Europe MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4 Asia Pacific
11.4.1 Trends Analysis
11.4.2 Asia Pacific MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.4.3 Asia Pacific MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.4 Asia Pacific MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.5 Asia Pacific MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.6 Asia Pacific MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.7 China
11.4.7.1 China MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.7.2 China MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.7.3 China MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.7.4 China MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.8 India
11.4.8.1 India MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.8.2 India MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.8.3 India MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.8.4 India MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.9 Japan
11.4.9.1 Japan MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.9.2 Japan MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.9.3 Japan MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.9.4 Japan MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.10 South Korea
11.4.10.1 South Korea MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.10.2 South Korea MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.10.3 South Korea MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.10.4 South Korea MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.11 Vietnam
11.4.11.1 Vietnam MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.11.2 Vietnam MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.11.3 Vietnam MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.11.4 Vietnam MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.12 Singapore
11.4.12.1 Singapore MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.12.2 Singapore MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.12.3 Singapore MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.12.4 Singapore MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.13 Australia
11.4.13.1 Australia MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.13.2 Australia MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.13.3 Australia MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.13.4 Australia MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.4.14 Rest of Asia Pacific
11.4.14.1 Rest of Asia Pacific MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.4.14.2 Rest of Asia Pacific MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.4.14.3 Rest of Asia Pacific MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.4.14.4 Rest of Asia Pacific MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5 Middle East and Africa
11.5.1 Middle East
11.5.1.1 Trends Analysis
11.5.1.2 Middle East MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.5.1.3 Middle East MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.1.4 Middle East MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.1.5 Middle East MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.1.6 Middle East MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.1.7 UAE
11.5.1.7.1 UAE MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.1.7.2 UAE MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.1.7.3 UAE MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.1.7.4 UAE MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.1.8 Egypt
11.5.1.8.1 Egypt MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.1.8.2 Egypt MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.1.8.3 Egypt MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.1.8.4 Egypt MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.1.9 Saudi Arabia
11.5.1.9.1 Saudi Arabia MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.1.9.2 Saudi Arabia MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.1.9.3 Saudi Arabia MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.1.9.4 Saudi Arabia MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.1.10 Qatar
11.5.1.10.1 Qatar MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.1.10.2 Qatar MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.1.10.3 Qatar MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.1.10.4 Qatar MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.1.11 Rest of Middle East
11.5.1.11.1 Rest of Middle East MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.1.11.2 Rest of Middle East MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.1.11.3 Rest of Middle East MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.1.11.4 Rest of Middle East MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.2 Africa
11.5.2.1 Trends Analysis
11.5.2.2 Africa MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.5.2.3 Africa MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.2.4 Africa MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.2.5 Africa MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.2.6 Africa MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.2.7 South Africa
11.5.2.7.1 South Africa MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.2.7.2 South Africa MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.2.7.3 South Africa MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.2.7.4 South Africa MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.2.8 Nigeria
11.5.2.8.1 Nigeria MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.2.8.2 Nigeria MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.2.8.3 Nigeria MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.2.8.4 Nigeria MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.5.2.9 Rest of Africa
11.5.2.9.1 Rest of Africa MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.5.2.9.2 Rest of Africa MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.5.2.9.3 Rest of Africa MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.5.2.9.4 Rest of Afric MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.6 Latin America
11.6.1 Trends Analysis
11.6.2 Latin America MLOps Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
11.6.3 Latin America MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.6.4 Latin America MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.6.5 Latin America MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.6.6 Latin America MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.6.7 Brazil
11.6.7.1 Brazil MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.6.7.2 Brazil MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.6.7.3 Brazil MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.6.7.4 Brazil MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.6.8 Argentina
11.6.8.1 Argentina MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.6.8.2 Argentina MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.6.8.3 Argentina MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.6.8.4 Argentina MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.6.9 Colombia
11.6.9.1 Colombia MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.6.9.2 Colombia MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.6.9.3 Colombia MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.6.9.4 Colombia MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
11.6.10 Rest of Latin America
11.6.10.1 Rest of Latin America MLOps Market Estimates and Forecasts, By Component (2020-2032) (USD Billion)
11.6.10.2 Rest of Latin America MLOps Market Estimates and Forecasts, By Deployment (2020-2032) (USD Billion)
11.6.10.3 Rest of Latin America MLOps Market Estimates and Forecasts, By Organization Size (2020-2032) (USD Billion)
11.6.10.4 Rest of Latin America MLOps Market Estimates and Forecasts, By End-Use (2020-2032) (USD Billion)
12. Company Profiles
12.1 IBM Corporation
12.1.1 Company Overview
12.1.2 Financial
12.1.3 Product / Services Offered
12.1.4 SWOT Analysis
12.2 GAVS Technologies
12.2.1 Company Overview
12.2.2 Financial
12.2.3 Product / Services Offered
12.2.4 SWOT Analysis
12.3 Amazon Web Services, Inc.
12.3.1 Company Overview
12.3.2 Financial
12.3.3 Product / Services Offered
12.3.4 SWOT Analysis
12.4 Databricks, Inc.
12.4.1 Company Overview
12.4.2 Financial
12.4.3 Product / Services Offered
12.4.4 SWOT Analysis
12.5 DataRobot, Inc.
12.5.1 Company Overview
12.5.2 Financial
12.5.3 Product / Services Offered
12.5.4 SWOT Analysis
12.6 Microsoft Corporation
12.6.1 Company Overview
12.6.2 Financial
12.6.3 Product / Services Offered
12.6.4 SWOT Analysis
12.7 Cloudera, Inc.
12.7.1 Company Overview
12.7.2 Financial
12.7.3 Product / Services Offered
12.7.4 SWOT Analysis
12.8 Akira AI
12.8.1 Company Overview
12.8.2 Financial
12.8.3 Product / Services Offered
12.8.4 SWOT Analysis
12.9 Alteryx
12.9.1 Company Overview
12.9.2 Financial
12.9.3 Product / Services Offered
12.9.4 SWOT Analysis
12.10 Google LLC
12.10.1 Company Overview
12.10.2 Financial
12.10.3 Product/ Services Offered
12.10.4 SWOT Analysis
13. Use Cases and Best Practices
14. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
Key Segments:
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By Deployment
On-Premise
Cloud
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Middle East
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Africa
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The System Integration Market size was valued at USD 434.47 Billion in 2023 and is expected to reach USD 1046.9 Billion by 2032 and grow at a CAGR of 9.61% over the forecast period 2024-2032.
The IoT Connectivity Market was USD 8.43 billion in 2023 and is expected to reach USD 51.51 billion by 2032, growing at a CAGR of 22.33% by 2024-2032.
The Geomarketing Market Size was valued at USD 17.80 Billion in 2023 and is expected to reach USD 121.56 Billion by 2032 and grow at a CAGR of 25.22% over the forecast period 2024-2032.
The GCC in the Retail and Consumer Goods Market size was USD 19.1 Billion in 2023, Will Reach to USD 76.9 Bn by 2032 & grow at a CAGR of 15.1% by 2024-2032.
Customer Intelligence Platform Market was valued at USD 2.5 billion and is expected to reach USD 22.1 billion by 2032, growing at a CAGR of 27.4% over 2024-2032
The E-tailing Solutions Market size was valued at USD 18.21 Billion in 2023 and is expected to reach USD 56.77 Billion by 2032 and grow at a CAGR of 13.39% over the forecast period 2024-2032.
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