image

REPORT SCOPE & OVERVIEW

The MLOps Market size was USD 1.18 billion in 2022 and is expected to Reach USD 17.4 billion by 2030 and grow at a CAGR of 40 % over the forecast period of 2023-2030

MLOps is a set of practices that combines machine learning and DevOps to automate the end-to-end machine learning lifecycle, from data preparation to model deployment and monitoring. By automating the ML lifecycle, MLOps can help organizations to Deploy models more quickly and efficiently. Improve the accuracy and performance of models. Reduce the risk of bias in models. Monitor models for performance and drift. MLOps is essential for organizations that want to successfully deploy and scale machine learning models in production. MLOps is a rapidly evolving field, and there are new tools and techniques being developed all the time. As MLOps matures, it is becoming an essential part of the machine learning toolkit. The most popular MLOps tools are Databricks MLflow, Amazon Sage Maker, and Google Cloud ML Engine. The number of MLOps jobs is projected to grow by 300% by 2023. MLOps give SMEs access to robust predictive models that can spur growth and keep them competitive in the market. For instance, the start-up AI company OmniML announced the release of Omnimizer in June 2022. The new platform bridges the gap between edge hardware and ML models to streamline and enhance MLOps. MLOps is being adopted by various industries, including healthcare, IT, retail, and more. This widespread adoption creates lucrative growth opportunities as organizations recognize the benefits of MLOps and seek to implement it in their operations. In May 2021, Google LLC launched an MLOps solution, Vertex AI a managed AI platform. It’s designed to help companies to accelerate the deployment and maintenance of AI models, Google says, by requiring nearly 80% fewer lines of code to train a model versus competitive platforms. The growth of the MLOps market also translates into career opportunities for professionals with expertise in machine learning, data engineering, software development, and operations management. Organizations are seeking skilled individuals who can effectively implement and manage MLOps processes.

MLOps Market Revenue Analysis

MARKET DYNAMICS

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.

 The rise of AI and machine learning is driving the demand for MLOps solutions. Organizations are increasingly using ML to automate tasks, improve decision-making, and gain insights from data. MLOps helps organizations to deploy and scale ML models in production, which is essential for realizing the benefits of AI.

RESTRAIN

  • Complexity of MLOps

  • There is still a lack of awareness about MLOps among many organizations.

MLOps is a complex and challenging discipline. It requires a deep understanding of both ML and DevOps. This can be a barrier for some organizations, especially those that are new to ML.                                   

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.

CHALLENGES

  • Data quality is a critical factor for MLOps.

  • Model deployment is another major challenge for MLOps. It can be difficult to deploy ML models in production and ensure that they are working correctly.

If the data is not of high quality, then the ML models will not be accurate. This is a major challenge for organizations, as it can be difficult to ensure that the data is of high quality.

IMPACT OF RUSSIAN UKRAINE WAR

The MLOps business has been greatly affected by the Russian-Ukrainian conflict. The supply chain for key crucial MLOps components, like GPUs and cloud computing resources, has been hampered by the war. As a result, these components now have higher prices and longer lead times, which makes it more challenging for MLOps teams to install and scale their models. There is a talent shortage in the MLOps business as a result of the war's forced emigration of many Ukrainian IT workers. For MLOps teams, this has made it increasingly challenging to identify and hire qualified engineers. The number of Ukrainian IT specialists employed by the MLOps sector has fallen by 20%. Cyberattacks on MLOps teams have increased by 30% in number. According to Google Cloud, the conflict has "significantly disrupted" their business activities in Ukraine. Some of the company's employees had to relocate, and the demand for its security products has grown. According to Google Cloud, the conflict has "significantly disrupted" their business activities in Ukraine. Some of the company's employees had to relocate, and the demand for its security products has grown. The sector, though, is adaptable and will rise to the new difficulties. In the long run, as businesses look for ways to increase their security and resilience, the fight may potentially hasten the adoption of MLOps.

IMPACT OF ONGOING RECESSION

The MLOps market is suffering from the continuing recession. Leading businesses in the industry are announcing slower growth, layoffs, and budget reductions. For instance, a well-known MLOps platform, Databricks, recently disclosed that 200 people would be let go. In the first quarter of 2023, global investment in MLOps decreased by 20%, according to a new analysis by CB Insights. In the first quarter of 2023, the number of job posts for MLOps roles decreased by 10%. In the first quarter of 2023, the number of M&A transactions in the MLOps sector decreased by 25%. The market's long-term forecast, though, is still favourable. For companies that wish to deploy and expand machine learning models, MLOps is an essential piece of technology. As the demand for machine learning continues to grow, so will the demand for MLOps solutions.

KEY MARKET SEGMENTS

By Component

  • Platform

  • Service

By Deployment 

  • On-Premise

  • Cloud

By Organization Size 

  • Large Enterprise

  • Small and Medium Size Enterprise

By End-Use Vertical

  • IT

  • Telecom Services

  • Government

  • BFSI

  • Retail

  • Consumer Goods

  • Transportation

  • Others

MLOps Market Segmentation Analysis

Region Coverage:

North America

  • USA

  • 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 the Middle East

  • Africa

    • Nigeria

    • South Africa

    • Rest of Africa

Latin America

  • Brazil

  • Argentina

  • Colombia

  • Rest of Latin America

REGIONAL ANALYSIS

North America dominated the market in 2022, accounting for over 41% 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. In order 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 quickest CAGR Over the forecast period. The market for 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.

KEY PLAYERS

The major key players in the MLOps Market are IBM Corporation, GAVS Technologies, Amazon Web Services, Inc., Databricks, Inc., DataRobot, Inc., Microsoft Corporation, Cloudera, Inc., Akira AI, Alteryx, Google LLC, and other players.

Amazon Web Services Inc-Company Financial Analysis

RECENT DEVELOPMENTS

Canonical Ltd:

In April 2023, Canonical Ltd., a computer software company, announced the launch of 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.

DataRobot:

In October 2021, DataRobot Released a new version of its DataRobot platform with new features for MLOps, including model monitoring and explainability.

MLOps Market Report Scope:
Report Attributes Details
Market Size in 2022  US$ 1.18Bn
Market Size by 2030  US$ 17.4 Bn
CAGR   CAGR of 40% From 2023 to 2030
Base Year 2022
Forecast Period  2023-2030
Historical Data  2020-2021
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.

 

Frequently Asked Questions

Ans. The Compound Annual Growth rate for MLOps Market over the forecast period is 40%.

Ans. USD 17.4 Billion is the Company's projected MLOps Market size by 2030.

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.

TABLE OF CONTENT

1. Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions

2. Research Methodology

3. Market Dynamics
3.1 Drivers
3.2 Restraints
3.3 Opportunities
3.4 Challenges

4. Impact Analysis
4.1 Impact of Ukraine- Russia War
4.2 Impact of Recession
4.2.2.1 US
4.2.2.2 Canada
4.2.2.3 Germany
4.2.2.4 France
4.2.2.5 United Kingdom
4.2.2.6 China
4.2.2.7 Japan
4.2.2.8 South Korea
4.2.2.9 Rest of the World

5. Value Chain Analysis

6. Porter’s 5 forces model

7. PEST Analysis

8. MLOps Market Segmentation, by Component
8.1 Platform
8.2 Service

9. MLOps Market Segmentation, by Deployment
9.1 On-Premise
9.2 Cloud

10. MLOps Market Segmentation, by Organization Size
10.1 Large Enterprise
10.2 Small and Medium Enterprise

11. MLOps Market Segmentation, by End-Use Vertical
11.1 IT
11.2 Telecom Services
11.3 Government
11.4 BFSI
11.5 Retail
11.6 Consumer Goods
11.7 Transportation
11.8 Others

12. Regional Analysis
12.1 Introduction
12.2 North America
12.2.1 North America MLOps Market by Country
12.2.2North America MLOps Market by Component
12.2.3 North America MLOps Market by Deployment
12.2.4 North America MLOps Market by Organization Size
12.2.5 North America MLOps Market by End-Use Vertical
12.2.6 USA
12.2.6.1 USA MLOps Market by Component
12.2.6.2 USA MLOps Market by Deployment
12.2.6.3 USA MLOps Market by Organization Size
12.2.6.4 USA MLOps Market by End-Use Vertical
12.2.7 Canada
12.2.7.1 Canada MLOps Market by Component
12.2.7.2 Canada MLOps Market by Deployment
12.2.7.3 Canada MLOps Market by Organization Size
12.2.7.4 Canada MLOps Market by End-Use Vertical
12.2.8 Mexico
12.2.8.1 Mexico MLOps Market by Component
12.2.8.2 Mexico MLOps Market by Deployment
12.2.8.3 Mexico MLOps Market by Organization Size
12.2.8.4 Mexico MLOps Market by End-Use Vertical
12.3 Europe
12.3.1 Eastern Europe
12.3.1.1 Eastern Europe MLOps Market by Country
12.3.1.2 Eastern Europe MLOps Market by Component
12.3.1.3 Eastern Europe MLOps Market by Deployment
12.3.1.4 Eastern Europe MLOps Market by Organization Size
12.3.1.5 Eastern Europe MLOps Market by End-Use Vertical
12.3.1.6 Poland
12.3.1.6.1 Poland MLOps Market by Component
12.3.1.6.2 Poland MLOps Market by Deployment
12.3.1.6.3 Poland MLOps Market by Organization Size
12.3.1.6.4 Poland MLOps Market by End-Use Vertical
12.3.1.7 Romania
12.3.1.7.1 Romania MLOps Market by Component
12.3.1.7.2 Romania MLOps Market by Deployment
12.3.1.7.3 Romania MLOps Market by Organization Size
12.3.1.7.4 Romania MLOps Market by End-Use Vertical
12.3.1.8 Hungary
12.3.1.8.1 Hungary MLOps Market by Component
12.3.1.8.2 Hungary MLOps Market by Deployment
12.3.1.8.3 Hungary MLOps Market by Organization Size
12.3.1.8.4 Hungary MLOps Market by End-Use Vertical
12.3.1.9 Turkey
12.3.1.9.1 Turkey MLOps Market by Component
12.3.1.9.2 Turkey MLOps Market by Deployment
12.3.1.9.3 Turkey MLOps Market by Organization Size
12.3.1.9.4 Turkey MLOps Market by End-Use Vertical
12.3.1.10 Rest of Eastern Europe
12.3.1.10.1 Rest of Eastern Europe MLOps Market by Component
12.3.1.10.2 Rest of Eastern Europe MLOps Market by Deployment
12.3.1.10.3 Rest of Eastern Europe MLOps Market by Organization Size
12.3.1.10.4 Rest of Eastern Europe MLOps Market by End-Use Vertical
12.3.2 Western Europe
12.3.2.1 Western Europe MLOps Market by Country
12.3.2.2 Western Europe MLOps Market by Component
12.3.2.3 Western Europe MLOps Market by Deployment
12.3.2.4 Western Europe MLOps Market by Organization Size
12.3.2.5 Western Europe MLOps Market by End-Use Vertical
12.3.2.6 Germany
12.3.2.6.1 Germany MLOps Market by Component
12.3.2.6.2 Germany MLOps Market by Deployment
12.3.2.6.3 Germany MLOps Market by Organization Size
12.3.2.6.4 Germany MLOps Market by End-Use Vertical
12.3.2.7 France
12.3.2.7.1 France MLOps Market by Component
12.3.2.7.2 France MLOps Market by Deployment
12.3.2.7.3 France MLOps Market by Organization Size
12.3.2.7.4 France MLOps Market by End-Use Vertical
12.3.2.8 UK
12.3.2.8.1 UK MLOps Market by Component
12.3.2.8.2 UK MLOps Market by Deployment
12.3.2.8.3 UK MLOps Market by Organization Size
12.3.2.8.4 UK MLOps Market by End-Use Vertical
12.3.2.9 Italy
12.3.2.9.1 Italy MLOps Market by Component
12.3.2.9.2 Italy MLOps Market by Deployment
12.3.2.9.3 Italy MLOps Market by Organization Size
12.3.2.9.4 Italy MLOps Market by End-Use Vertical
12.3.2.10 Spain
12.3.2.10.1 Spain MLOps Market by Component
12.3.2.10.2 Spain MLOps Market by Deployment
12.3.2.10.3 Spain MLOps Market by Organization Size
12.3.2.10.4 Spain MLOps Market by End-Use Vertical
12.3.2.11 Netherlands
12.3.2.11.1 Netherlands MLOps Market by Component
12.3.2.11.2 Netherlands MLOps Market by Deployment
12.3.2.11.3 Netherlands MLOps Market by Organization Size
12.3.2.11.4 Netherlands MLOps Market by End-Use Vertical
12.3.2.12 Switzerland
12.3.2.12.1 Switzerland MLOps Market by Component
12.3.2.12.2 Switzerland MLOps Market by Deployment
12.3.2.12.3 Switzerland MLOps Market by Organization Size
12.3.2.12.4 Switzerland MLOps Market by End-Use Vertical
12.3.2.13 Austria
12.3.2.13.1 Austria MLOps Market by Component
12.3.2.13.2 Austria MLOps Market by Deployment
12.3.2.13.3 Austria MLOps Market by Organization Size
12.3.2.13.4 Austria MLOps Market by End-Use Vertical
12.3.2.14 Rest of Western Europe
12.3.2.14.1 Rest of Western Europe MLOps Market by Component
12.3.2.14.2 Rest of Western Europe MLOps Market by Deployment
12.3.2.14.3 Rest of Western Europe MLOps Market by Organization Size
12.3.2.14.4 Rest of Western Europe MLOps Market by End-Use Vertical
12.4 Asia-Pacific
12.4.1 Asia Pacific MLOps Market by Country
12.4.2 Asia Pacific MLOps Market by Component
12.4.3 Asia Pacific MLOps Market by Deployment
12.4.4 Asia Pacific MLOps Market by Organization Size
12.4.5 Asia Pacific MLOps Market by End-Use Vertical
12.4.6 China
12.4.6.1 China MLOps Market by Component
12.4.6.2 China MLOps Market by Deployment
12.4.6.3 China MLOps Market by Organization Size
12.4.6.4 China MLOps Market by End-Use Vertical
12.4.7 India
12.4.7.1 India MLOps Market by Component
12.4.7.2 India MLOps Market by Deployment
12.4.7.3 India MLOps Market by Organization Size
12.4.7.4 India MLOps Market by End-Use Vertical
12.4.8 Japan
12.4.8.1 Japan MLOps Market by Component
12.4.8.2 Japan MLOps Market by Deployment
12.4.8.3 Japan MLOps Market by Organization Size
12.4.8.4 Japan MLOps Market by End-Use Vertical
12.4.9 South Korea
12.4.9.1 South Korea MLOps Market by Component
12.4.9.2 South Korea MLOps Market by Deployment
12.4.9.3 South Korea MLOps Market by Organization Size
12.4.9.4 South Korea MLOps Market by End-Use Vertical
12.4.10 Vietnam
12.4.10.1 Vietnam MLOps Market by Component
12.4.10.2 Vietnam MLOps Market by Deployment
12.4.10.3 Vietnam MLOps Market by Organization Size
12.4.10.4 Vietnam MLOps Market by End-Use Vertical
12.4.11 Singapore
12.4.11.1 Singapore MLOps Market by Component
12.4.11.2 Singapore MLOps Market by Deployment
12.4.11.3 Singapore MLOps Market by Organization Size
12.4.11.4 Singapore MLOps Market by End-Use Vertical
12.4.12 Australia
12.4.12.1 Australia MLOps Market by Component
12.4.12.2 Australia MLOps Market by Deployment
12.4.12.3 Australia MLOps Market by Organization Size
12.4.12.4 Australia MLOps Market by End-Use Vertical
12.4.13 Rest of Asia-Pacific
12.4.13.1 Rest of Asia-Pacific MLOps Market by Component
12.4.13.2 Rest of Asia-Pacific APAC MLOps Market by Deployment
12.4.13.3 Rest of Asia-Pacific MLOps Market by Organization Size
12.4.13.4 Rest of Asia-Pacific MLOps Market by End-Use Vertical
12.5 Middle East & Africa
12.5.1 Middle East
12.5.1.1 Middle East MLOps Market by Country
12.5.1.2 Middle East MLOps Market by Component
12.5.1.3 Middle East MLOps Market by Deployment
12.5.1.4 Middle East MLOps Market by Organization Size
12.5.1.5 Middle East MLOps Market by End-Use Vertical
12.5.1.6 UAE
12.5.1.6.1 UAE MLOps Market by Component
12.5.1.6.2 UAE MLOps Market by Deployment
12.5.1.6.3 UAE MLOps Market by Organization Size
12.5.1.6.4 UAE MLOps Market by End-Use Vertical
12.5.1.7 Egypt
12.5.1.7.1 Egypt MLOps Market by Component
12.5.1.7.2 Egypt MLOps Market by Deployment
12.5.1.7.3 Egypt MLOps Market by Organization Size
12.5.1.7.4 Egypt MLOps Market by End-Use Vertical
12.5.1.8 Saudi Arabia
12.5.1.8.1 Saudi Arabia MLOps Market by Component
12.5.1.8.2 Saudi Arabia MLOps Market by Deployment
12.5.1.8.3 Saudi Arabia MLOps Market by Organization Size
12.5.1.8.4 Saudi Arabia MLOps Market by End-Use Vertical
12.5.1.9 Qatar
12.5.1.9.1 Qatar MLOps Market by Component
12.5.1.9.2 Qatar MLOps Market by Deployment
12.5.1.9.3 Qatar MLOps Market by Organization Size
12.5.1.9.4 Qatar MLOps Market by End-Use Vertical
12.5.1.10 Rest of Middle East
12.5.1.10.1 Rest of Middle East MLOps Market by Component
12.5.1.10.2 Rest of Middle East MLOps Market by Deployment
12.5.1.10.3 Rest of Middle East MLOps Market by Organization Size
12.5.1.10.4 Rest of Middle East MLOps Market by End-Use Vertical
12.5.2. Africa
12.5.2.1 Africa MLOps Market by Country
12.5.2.2 Africa MLOps Market by Component
12.5.2.3 Africa MLOps Market by Deployment
12.5.2.4 Africa MLOps Market by Organization Size
12.5.2.5 Africa MLOps Market by End-Use Vertical
12.5.2.6 Nigeria
12.5.2.6.1 Nigeria MLOps Market by Component
12.5.2.6.2 Nigeria MLOps Market by Deployment
12.5.2.6.3 Nigeria MLOps Market by Organization Size
12.5.2.6.4 Nigeria MLOps Market by End-Use Vertical
12.5.2.7 South Africa
12.5.2.7.1 South Africa MLOps Market by Component
12.5.2.7.2 South Africa MLOps Market by Deployment
12.5.2.7.3 South Africa MLOps Market by Organization Size
12.5.2.7.4 South Africa MLOps Market by End-Use Vertical
12.5.2.8 Rest of Africa
12.5.2.8.1 Rest of Africa MLOps Market by Component
12.5.2.8.2 Rest of Africa MLOps Market by Deployment
12.5.2.8.3 Rest of Africa MLOps Market by Organization Size
12.5.2.8.4 Rest of Africa MLOps Market by End-Use Vertical
12.6. Latin America
12.6.1 Latin America MLOps Market by Country
12.6.2 Latin America MLOps Market by Component
12.6.3 Latin America MLOps Market by Deployment
12.6.4 Latin America MLOps Market by Organization Size
12.6.5 Latin America MLOps Market by End-Use Vertical
12.6.6 Brazil
12.6.6.1 Brazil MLOps Market by Component
12.6.6.2 Brazil Africa MLOps Market by Deployment
12.6.6.3 Brazil MLOps Market by Organization Size
12.6.6.4 Brazil MLOps Market by End-Use Vertical
12.6.7 Argentina
12.6.7.1 Argentina MLOps Market by Component
12.6.7.2 Argentina MLOps Market by Deployment
12.6.7.3 Argentina MLOps Market by Organization Size
12.6.7.4 Argentina MLOps Market by End-Use Vertical
12.6.8 Colombia
12.6.8.1 Colombia MLOps Market by Component
12.6.8.2 Colombia MLOps Market by Deployment
12.6.8.3 Colombia MLOps Market by Organization Size
12.6.8.4 Colombia MLOps Market by End-Use Vertical
12.6.9 Rest of Latin America
12.6.9.1 Rest of Latin America MLOps Market by Component
12.6.9.2 Rest of Latin America MLOps Market by Deployment
12.6.9.3 Rest of Latin America MLOps Market by Organization Size
12.6.9.4 Rest of Latin America MLOps Market by End-Use Vertical

13 Company profile
13.1 IBM Corporation
13.1.1 Company Overview
13.1.2 Financials
13.1.3Product/Services/Offerings
13.1.4 SWOT Analysis
13.1.5 The SNS View
13.2 GAVS Technologies
13.2.1 Company Overview
13.2.2 Financials
13.2.3Product/Services/Offerings
13.2.4 SWOT Analysis
13.2.5 The SNS View
13.3 Amazon Web Services, Inc.
13.3.1 Company Overview
13.3.2 Financials
13.3.3Product/Services/Offerings
13.3.4 SWOT Analysis
13.3.5 The SNS View
13.4 Databricks, Inc.
13.4.1 Company Overview
13.4.2 Financials
13.4.3Product/Services/Offerings
13.4.4 SWOT Analysis
13.4.5 The SNS View
13.5 DataRobot Inc.
13.5.1 Company Overview
13.5.2 Financials
13.5.3Product/Services/Offerings
13.5.4 SWOT Analysis
13.5.5 The SNS View
13.6 Microsoft Corporation
13.6.1 Company Overview
13.6.2 Financials
13.6.3Product/Services/Offerings
13.6.4 SWOT Analysis
13.6.5 The SNS View
13.7 Cloudera, Inc.
13.7.1 Company Overview
13.7.2 Financials
13.7.3Product/Services/Offerings
13.7.4 SWOT Analysis
13.7.5 The SNS View
13.8 Akira AI
13.8.1 Company Overview
13.8.2 Financial
13.8.3Product/Services/Offerings
13.8.4 SWOT Analysis
13.8.5 The SNS View
13.9 Alteryx
13.9.1 Company Overview
13.9.2 Financials
13.9.3 Product/Service/Offerings
13.9.4 SWOT Analysis
13.9.5 The SNS View
13.10 Google LLC
13.10.1 Company Overview
13.10.2 Financials
13.10.3 Product/Service/Offerings
13.10.4 SWOT Analysis
13.10.5 The SNS View

14. Competitive Landscape
14.1 Competitive Benchmarking
14.2 Company Share Analysis
14.3 Recent Developments
14.3.1 End-Use News
14.3.2 Company News
14.3.3 Mergers & Acquisitions

15. USE Cases and Best Practices

16. 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.

Secondary Research

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.

Primary Research

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.

Data Bank Validation

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.

Start a Conversation

Hi! Click one of our member below to chat on Phone