Region:Global
Author(s):Naman Rohilla
Product Code:KROD1429
The global Automated Machine Learning market is segmented by type, application, and region.
Company |
Establishment Year |
Headquarters |
DataRobot |
2012 |
Boston, USA |
H2O.ai |
2012 |
Mountain View, USA |
Google LLC |
1998 |
Mountain View, USA |
Microsoft Corporation |
1975 |
Redmond, USA |
IBM Corporation |
1911 |
Armonk, USA |
The Global Automated Machine Learning Market is expected to grow robustly over the coming years, driven by increasing demand for AI-powered automation, rising applications across various industries, and supportive government initiatives.
By Type |
Cloud-Based On-Premises |
By Application |
Finance Healthcare Retail Manufacturing Others |
By Region |
North America Europe Asia-Pacific (APAC) Middle East & Africa (MEA) Latin America |
1.1. Definition and Scope
1.2. Market Taxonomy
1.3. Market Growth Rate
1.4. Market Segmentation Overview
2.1. Historical Market Size
2.2. Year-on-Year Growth Analysis
2.3. Key Market Developments and Milestones
3.1. Growth Drivers
3.1.1. Increasing Demand for Automated Solutions
3.1.2. Growing Applications in AI and Analytics
3.1.3. Reduction in Complexity and Cost
3.2. Restraints
3.2.1. Data Privacy and Security Concerns
3.2.2. Limited Awareness in Emerging Markets
3.3. Opportunities
3.3.1. Advancements in AI Model Explainability
3.3.2. Increased Use in Predictive Analytics
3.4. Trends
3.4.1. Increased Focus on Ethical AI Practices
3.4.2. Integration with Cloud Computing Technologies
3.5. Government Regulation
3.5.1. EU AI Act
3.5.2. US National AI Initiative
3.6. SWOT Analysis
3.7. Stakeholder Ecosystem
3.8. Competition Ecosystem
4.1. By Type (in Value %)
4.1.1. Cloud-Based
4.1.2. On-Premises
4.2. By Application (in Value %)
4.2.1. Finance
4.2.2. Healthcare
4.2.3. Retail
4.2.4. Manufacturing
4.2.5. Others
4.3. By Region (in Value %)
4.3.1. North America
4.3.2. Europe
4.3.3. Asia-Pacific (APAC)
4.3.4. Middle East & Africa (MEA)
4.3.5. Latin America
5.1. Detailed Profiles of Major Companies
5.1.1. DataRobot
5.1.2. H2O.ai
5.1.3. Google LLC
5.1.4. Microsoft Corporation
5.1.5. IBM Corporation
5.1.6. Amazon Web Services (AWS)
5.1.7. Alteryx, Inc.
5.1.8. RapidMiner
5.1.9. TIBCO Software Inc.
5.1.10. Databricks
5.1.11. BigML, Inc.
5.1.12. Peltarion
5.1.13. SigOpt
5.1.14. Aible
5.1.15. dotData
5.2. Cross Comparison Parameters (No. of Employees, Headquarters, Inception Year, Revenue)
6.1. Market Share Analysis
6.2. Strategic Initiatives
6.3. Mergers and Acquisitions
6.4. Investment Analysis
6.4.1. Venture Capital Funding
6.4.2. Government Grants
6.4.3. Private Equity Investments
7.1. Environmental Regulations
7.2. Compliance Requirements
7.3. Certification Processes
8.1. Future Market Size Projections
8.2. Key Factors Driving Future Market Growth
9.1. By Type (in Value %)
9.2. By Application (in Value %)
9.3. By Region (in Value %)
10.1. TAM/SAM/SOM Analysis
10.2. Customer Cohort Analysis
10.3. Marketing Initiatives
10.4. White Space Opportunity Analysis
Ecosystem creation for all the major entities and referring to multiple secondary and proprietary databases to perform desk research around the market to collate market-level information.
Collating statistics on the Global Automated Machine Learning market over the years and analyzing the penetration of products as well as the ratio of suppliers to compute the revenue generated for the market. We will also review product quality statistics to ensure accuracy behind the data points shared.
Building market hypotheses and conducting CATIs with market experts from different companies to validate statistics and seek operational and financial information from company representatives.
Our team will approach multiple Automated Machine Learning processing companies and understand the nature of product segments and sales, consumer preference, and other parameters, which will support us in validating statistics derived through the bottom-to-top approach from Automated Machine Learning product manufacturers.
The Global Automated Machine Learning Market was valued at USD 1 billion in 2023, driven by the increasing demand for AI-powered automation, the growing use of AutoML in various industries, and the need to reduce the complexity of traditional machine learning processes.
The major players in the global autoML market include DataRobot, H2O.ai, Google LLC, Microsoft Corporation, and IBM Corporation. These companies are leaders in the development of AutoML platforms, focusing on improving usability, scalability, and integration with existing enterprise systems.
The growth drivers of the Global AutoML market include the increasing demand for automated data science solutions, the growing applications of AI and analytics across industries, and the need to reduce the complexity and cost associated with traditional machine learning processes.
The Global AutoML market faces challenges such as data privacy and security concerns, particularly with cloud-based solutions, and limited awareness of AutoML benefits and applications in emerging markets, which can hinder market growth.
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