The automated machine learning market had an estimated market share worth US$ 700 million in 2023, and it is predicted to reach a global market valuation of US$ 42.2 billion by 2034, growing at a steady CAGR of 44.9% from 2024 to 2034.
The market for automated machine learning is being driven by rising customer demand for comfort and lavish features in cars. Temperature sensors make it possible for advanced temperature control systems to function, guaranteeing each individual level of comfort and satisfying the increasing consumer demands for high-end driving experiences.
Automated Machine Learning Demand Outlook
| Report Attribute | Details |
|---|---|
| Estimated Market Value for 2023 | US$ 700 million |
| Expected Market Value for 2024 | US$ 1 billion |
| Projected Forecast Value for 2034 | US$ 42.2 billion |
| Anticipated Growth Rate from 2024 to 2034 | 44.9% CAGR |
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The global demand for automated machine learning market was estimated to reach a valuation of US$ 0.1 billion in 2019, according to a report from Future Market Insights (FMI). From 2019 to 2023, sales witnessed significant growth in the automated machine learning market, registering a CAGR of 48.2%.
| Historical CAGR from 2019 to 2023 | 48.2% |
|---|---|
| Forecast CAGR from 2024 to 2034 | 44.9% |
A driving factor is the growing intricacy of datasets, which calls for sophisticated analytics methods. Organizations may effectively extract important insights by using automated machine learning to handle a variety of data types, including unstructured, semi-structured, and structured data. This expertise is essential for being competitive in data-rich contexts across sectors.
Some important factors that will boost the market growth through 2034 are:
Shortage of Skilled Data Scientists to Push Companies towards Automated Machine Learning
The need to address the scarcity of qualified data scientists as well as machine learning specialists is a significant factor driving the market for automated machine learning. With the exponential expansion of data, the need for data-driven insights is outpacing the availability of expertise to meet that demand.
Automated machine learning platforms democratize AI by allowing non-experts to successfully design and deploy machine learning models. These platforms enable organizations to exploit AI technology without depending only on expensive and limited knowledge by automating repetitive jobs and simplifying complicated procedures. This accelerates the adoption of machine learning across multiple sectors.
Principal Consultant
The growing need for data-driven decision-making across sectors is one of the factors driving the market for automated machine learning. Automated machine learning streamlines the model development process, which helps firms get useful insights from large datasets.
Organizations may gain a competitive advantage and accelerate the deployment of machine learning models by automating processes like feature engineering, data preparation, and model selection. The increased understanding of AI's ability to promote innovation and improve operational efficiency is another factor driving this need.
Data Privacy and Decoding Complicated Data to Impede Market Growth
The market for automated machine learning is constrained by issues with data privacy, the difficulty of interpreting complicated models, and the lack of qualified workers who can use AI technology efficiently. Widespread adoption is further hampered by difficulties integrating automated machine learning into current workflows and the requirement for open regulatory frameworks.
To fully realize the potential of automated machine learning across companies, overcoming these obstacles will require resolving ethical issues, improving model interpretability, and funding educational and training programs.
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This section focuses on providing detailed analysis of two particular market segments for automated machine learning, the dominant solution type and the significant automation type. The two main segments discussed below are standalone solution and feature engineering type.
| Solution Type | Standalone |
|---|---|
| CAGR from 2024 to 2034 | 44.7% |
During the forecast period, the standalone segment is likely to garner a 44.7% CAGR. Standalone automated machine learning will gain popularity as firms seek simplified AI solutions that do not require large infrastructure. With their intuitive interfaces and comprehensive automation, these stand-alone systems let companies quickly implement machine learning models with no technical knowledge.
These technologies, which emphasize the democratization of AI, enable users from all industries to take use of sophisticated analytics, promoting efficiency and creativity. Standalone solutions are flexible and scalable, meeting a range of business requirements and hastening the global adoption of AI technology.
| Automation Type | Feature Engineering |
|---|---|
| Market Share in 2024 | 44.5% |
In 2024, the feature engineering segment is likely to acquire a 52.4% global market share. Feature engineering will gain popularity in the global automated machine learning market owing to its importance in improving model performance and interpretability. With organizations placing a growing emphasis on gleaning insightful information from intricate datasets, automated feature engineering techniques will become indispensable.
These tools allow speedier model building and deployment by automating the process of choosing, manipulating, and producing features. This helps firms stay competitive in a data-driven environment and extract actionable insights more quickly.
The markets for automated machine learning in a few significant countries, including the United States, the United Kingdom, China, Japan, and South Korea, will be covered in detail in this section. The section will focus on the primary reasons driving up demand for automated machine learning in these countries.
| Countries | CAGR from 2024 to 2034 |
|---|---|
| The United States | 45% |
| The United Kingdom | 46.1% |
| China | 45.4% |
| Japan | 46% |
| South Korea | 47.2% |
The United States automated machine learning is anticipated to gain a CAGR of 45% through 2034. Factors that are bolstering the growth are:
The market in the United Kingdom is expected to expand with a 46.1% CAGR through 2034. The factors pushing the growth are:
The automated machine learning ecosystem in China is anticipated to develop with a 45.4% CAGR from 2024 to 2034. The drivers behind this growth are:
The automated machine learning industry in Japan is anticipated to reach a 46% CAGR from 2024 to 2034. The drivers propelling growth forward are:
The automated machine learning ecosystem in South Korea is likely to attain a 47.2% CAGR during the forecast period. The factors bolstering the growth are:
Companies are concentrating on creating platforms and solutions to democratize and simplify the machine learning process in the global automated machine learning market. Their goal is to enable companies with less data science experience to take full advantage of artificial intelligence (AI). These companies provide tools for automating a range of processes, including as feature engineering, model selection, hyperparameter tweaking, and data preparation.
They provide services to a range of sectors, including manufacturing, healthcare, retail, and finance, allowing them to effectively gather insights and make data-driven choices. These companies offer adaptable solutions designed to meet particular company requirements, placing a strong emphasis on user-friendly interfaces and reliable performance to increase accessibility and adoption. The key players in this market include:
Significant advancements in the automated machine learning sector are being made by key market participants, and these include:
| Report Attribute | Details |
|---|---|
| Growth Rate | CAGR of 44.9% from 2024 to 2034 |
| Market value in 2024 | US$ 1 billion |
| Market value in 2034 | US$ 42.2 billion |
| Base Year for Estimation | 2023 |
| Historical Data | 2019 to 2023 |
| Forecast Period | 2024 to 2034 |
| Quantitative Units | US$ billion for value |
| Report Coverage | Revenue Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends, and Pricing Analysis |
| Segments Covered |
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| Regions Covered |
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| Countries Profiled |
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| Key Companies Profiled |
|
| Customization Scope | Available on Request |
The automated machine learning market is expected to garner a 44.9% CAGR from 2024 to 2034.
By 2024, the global automated machine learning market is likely to gain US$ 1 billion.
By 2034, the automated machine learning market valuation is likely to reach a sum of US$ 42.2 billion.
The automated machine learning industry in the United States is likely to garner a 45.0% CAGR during the forecast period.
The standalone automated machine learning solution will gain significance with a 44.7% CAGR through 2034.
By 2034, the feature engineering segment is anticipated to gain traction with a 44.5% CAGR from 2024 to 2034.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2019 to 2023 and Forecast, 2024 to 2034
4.1. Historical Market Size Value (US$ Million) Analysis, 2019 to 2023
4.2. Current and Future Market Size Value (US$ Million) Projections, 2024 to 2034
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Solution
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Solution, 2019 to 2023
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Solution, 2024 to 2034
5.3.1. Standalone
5.3.2. On-Premises
5.4. Y-o-Y Growth Trend Analysis By Solution, 2019 to 2023
5.5. Absolute $ Opportunity Analysis By Solution, 2024 to 2034
6. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Automation Type
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Automation Type, 2019 to 2023
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Automation Type, 2024 to 2034
6.3.1. Feature Engineering
6.3.2. Data Processing
6.3.3. Data Modelling
6.3.4. Visualization
6.3.5. Others
6.4. Y-o-Y Growth Trend Analysis By Automation Type, 2019 to 2023
6.5. Absolute $ Opportunity Analysis By Automation Type, 2024 to 2034
7. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By End-User
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By End-User, 2019 to 2023
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By End-User, 2024 to 2034
7.3.1. BFSI
7.3.2. Retail and E-Commerce
7.3.3. Healthcare
7.3.4. Manufacturing
7.3.5. Others
7.4. Y-o-Y Growth Trend Analysis By End-User, 2019 to 2023
7.5. Absolute $ Opportunity Analysis By End-User, 2024 to 2034
8. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Region
8.1. Introduction
8.2. Historical Market Size Value (US$ Million) Analysis By Region, 2019 to 2023
8.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2024 to 2034
8.3.1. North America
8.3.2. Latin America
8.3.3. Western Europe
8.3.4. Eastern Europe
8.3.5. South Asia and Pacific
8.3.6. East Asia
8.3.7. Middle East and Africa
8.4. Market Attractiveness Analysis By Region
9. North America Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
9.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
9.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
9.2.1. By Country
9.2.1.1. USA
9.2.1.2. Canada
9.2.2. By Solution
9.2.3. By Automation Type
9.2.4. By End-User
9.3. Market Attractiveness Analysis
9.3.1. By Country
9.3.2. By Solution
9.3.3. By Automation Type
9.3.4. By End-User
9.4. Key Takeaways
10. Latin America Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
10.2.1. By Country
10.2.1.1. Brazil
10.2.1.2. Mexico
10.2.1.3. Rest of Latin America
10.2.2. By Solution
10.2.3. By Automation Type
10.2.4. By End-User
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Solution
10.3.3. By Automation Type
10.3.4. By End-User
10.4. Key Takeaways
11. Western Europe Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
11.2.1. By Country
11.2.1.1. Germany
11.2.1.2. UK
11.2.1.3. France
11.2.1.4. Spain
11.2.1.5. Italy
11.2.1.6. Rest of Western Europe
11.2.2. By Solution
11.2.3. By Automation Type
11.2.4. By End-User
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Solution
11.3.3. By Automation Type
11.3.4. By End-User
11.4. Key Takeaways
12. Eastern Europe Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
12.2.1. By Country
12.2.1.1. Poland
12.2.1.2. Russia
12.2.1.3. Czech Republic
12.2.1.4. Romania
12.2.1.5. Rest of Eastern Europe
12.2.2. By Solution
12.2.3. By Automation Type
12.2.4. By End-User
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Solution
12.3.3. By Automation Type
12.3.4. By End-User
12.4. Key Takeaways
13. South Asia and Pacific Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
13.2.1. By Country
13.2.1.1. India
13.2.1.2. Bangladesh
13.2.1.3. Australia
13.2.1.4. New Zealand
13.2.1.5. Rest of South Asia and Pacific
13.2.2. By Solution
13.2.3. By Automation Type
13.2.4. By End-User
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Solution
13.3.3. By Automation Type
13.3.4. By End-User
13.4. Key Takeaways
14. East Asia Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
14.2.1. By Country
14.2.1.1. China
14.2.1.2. Japan
14.2.1.3. South Korea
14.2.2. By Solution
14.2.3. By Automation Type
14.2.4. By End-User
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Solution
14.3.3. By Automation Type
14.3.4. By End-User
14.4. Key Takeaways
15. Middle East and Africa Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
15.2.1. By Country
15.2.1.1. GCC Countries
15.2.1.2. South Africa
15.2.1.3. Israel
15.2.1.4. Rest of MEA
15.2.2. By Solution
15.2.3. By Automation Type
15.2.4. By End-User
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Solution
15.3.3. By Automation Type
15.3.4. By End-User
15.4. Key Takeaways
16. Key Countries Market Analysis
16.1. USA
16.1.1. Pricing Analysis
16.1.2. Market Share Analysis, 2023
16.1.2.1. By Solution
16.1.2.2. By Automation Type
16.1.2.3. By End-User
16.2. Canada
16.2.1. Pricing Analysis
16.2.2. Market Share Analysis, 2023
16.2.2.1. By Solution
16.2.2.2. By Automation Type
16.2.2.3. By End-User
16.3. Brazil
16.3.1. Pricing Analysis
16.3.2. Market Share Analysis, 2023
16.3.2.1. By Solution
16.3.2.2. By Automation Type
16.3.2.3. By End-User
16.4. Mexico
16.4.1. Pricing Analysis
16.4.2. Market Share Analysis, 2023
16.4.2.1. By Solution
16.4.2.2. By Automation Type
16.4.2.3. By End-User
16.5. Germany
16.5.1. Pricing Analysis
16.5.2. Market Share Analysis, 2023
16.5.2.1. By Solution
16.5.2.2. By Automation Type
16.5.2.3. By End-User
16.6. UK
16.6.1. Pricing Analysis
16.6.2. Market Share Analysis, 2023
16.6.2.1. By Solution
16.6.2.2. By Automation Type
16.6.2.3. By End-User
16.7. France
16.7.1. Pricing Analysis
16.7.2. Market Share Analysis, 2023
16.7.2.1. By Solution
16.7.2.2. By Automation Type
16.7.2.3. By End-User
16.8. Spain
16.8.1. Pricing Analysis
16.8.2. Market Share Analysis, 2023
16.8.2.1. By Solution
16.8.2.2. By Automation Type
16.8.2.3. By End-User
16.9. Italy
16.9.1. Pricing Analysis
16.9.2. Market Share Analysis, 2023
16.9.2.1. By Solution
16.9.2.2. By Automation Type
16.9.2.3. By End-User
16.10. Poland
16.10.1. Pricing Analysis
16.10.2. Market Share Analysis, 2023
16.10.2.1. By Solution
16.10.2.2. By Automation Type
16.10.2.3. By End-User
16.11. Russia
16.11.1. Pricing Analysis
16.11.2. Market Share Analysis, 2023
16.11.2.1. By Solution
16.11.2.2. By Automation Type
16.11.2.3. By End-User
16.12. Czech Republic
16.12.1. Pricing Analysis
16.12.2. Market Share Analysis, 2023
16.12.2.1. By Solution
16.12.2.2. By Automation Type
16.12.2.3. By End-User
16.13. Romania
16.13.1. Pricing Analysis
16.13.2. Market Share Analysis, 2023
16.13.2.1. By Solution
16.13.2.2. By Automation Type
16.13.2.3. By End-User
16.14. India
16.14.1. Pricing Analysis
16.14.2. Market Share Analysis, 2023
16.14.2.1. By Solution
16.14.2.2. By Automation Type
16.14.2.3. By End-User
16.15. Bangladesh
16.15.1. Pricing Analysis
16.15.2. Market Share Analysis, 2023
16.15.2.1. By Solution
16.15.2.2. By Automation Type
16.15.2.3. By End-User
16.16. Australia
16.16.1. Pricing Analysis
16.16.2. Market Share Analysis, 2023
16.16.2.1. By Solution
16.16.2.2. By Automation Type
16.16.2.3. By End-User
16.17. New Zealand
16.17.1. Pricing Analysis
16.17.2. Market Share Analysis, 2023
16.17.2.1. By Solution
16.17.2.2. By Automation Type
16.17.2.3. By End-User
16.18. China
16.18.1. Pricing Analysis
16.18.2. Market Share Analysis, 2023
16.18.2.1. By Solution
16.18.2.2. By Automation Type
16.18.2.3. By End-User
16.19. Japan
16.19.1. Pricing Analysis
16.19.2. Market Share Analysis, 2023
16.19.2.1. By Solution
16.19.2.2. By Automation Type
16.19.2.3. By End-User
16.20. South Korea
16.20.1. Pricing Analysis
16.20.2. Market Share Analysis, 2023
16.20.2.1. By Solution
16.20.2.2. By Automation Type
16.20.2.3. By End-User
16.21. GCC Countries
16.21.1. Pricing Analysis
16.21.2. Market Share Analysis, 2023
16.21.2.1. By Solution
16.21.2.2. By Automation Type
16.21.2.3. By End-User
16.22. South Africa
16.22.1. Pricing Analysis
16.22.2. Market Share Analysis, 2023
16.22.2.1. By Solution
16.22.2.2. By Automation Type
16.22.2.3. By End-User
16.23. Israel
16.23.1. Pricing Analysis
16.23.2. Market Share Analysis, 2023
16.23.2.1. By Solution
16.23.2.2. By Automation Type
16.23.2.3. By End-User
17. Market Structure Analysis
17.1. Competition Dashboard
17.2. Competition Benchmarking
17.3. Market Share Analysis of Top Players
17.3.1. By Regional
17.3.2. By Solution
17.3.3. By Automation Type
17.3.4. By End-User
18. Competition Analysis
18.1. Competition Deep Dive
18.1.1. Datarobot inc.
18.1.1.1. Overview
18.1.1.2. Product Portfolio
18.1.1.3. Profitability by Market Segments
18.1.1.4. Sales Footprint
18.1.1.5. Strategy Overview
18.1.1.5.1. Marketing Strategy
18.1.2. Amazon web services Inc.
18.1.2.1. Overview
18.1.2.2. Product Portfolio
18.1.2.3. Profitability by Market Segments
18.1.2.4. Sales Footprint
18.1.2.5. Strategy Overview
18.1.2.5.1. Marketing Strategy
18.1.3. dotData Inc.
18.1.3.1. Overview
18.1.3.2. Product Portfolio
18.1.3.3. Profitability by Market Segments
18.1.3.4. Sales Footprint
18.1.3.5. Strategy Overview
18.1.3.5.1. Marketing Strategy
18.1.4. IBM Corporation
18.1.4.1. Overview
18.1.4.2. Product Portfolio
18.1.4.3. Profitability by Market Segments
18.1.4.4. Sales Footprint
18.1.4.5. Strategy Overview
18.1.4.5.1. Marketing Strategy
18.1.5. Dataiku
18.1.5.1. Overview
18.1.5.2. Product Portfolio
18.1.5.3. Profitability by Market Segments
18.1.5.4. Sales Footprint
18.1.5.5. Strategy Overview
18.1.5.5.1. Marketing Strategy
18.1.6. SAS Institute Inc.
18.1.6.1. Overview
18.1.6.2. Product Portfolio
18.1.6.3. Profitability by Market Segments
18.1.6.4. Sales Footprint
18.1.6.5. Strategy Overview
18.1.6.5.1. Marketing Strategy
18.1.7. Microsoft Corporation
18.1.7.1. Overview
18.1.7.2. Product Portfolio
18.1.7.3. Profitability by Market Segments
18.1.7.4. Sales Footprint
18.1.7.5. Strategy Overview
18.1.7.5.1. Marketing Strategy
18.1.8. Google LLC
18.1.8.1. Overview
18.1.8.2. Product Portfolio
18.1.8.3. Profitability by Market Segments
18.1.8.4. Sales Footprint
18.1.8.5. Strategy Overview
18.1.8.5.1. Marketing Strategy
18.1.9. H2O.ai
18.1.9.1. Overview
18.1.9.2. Product Portfolio
18.1.9.3. Profitability by Market Segments
18.1.9.4. Sales Footprint
18.1.9.5. Strategy Overview
18.1.9.5.1. Marketing Strategy
18.1.10. Aible Inc.
18.1.10.1. Overview
18.1.10.2. Product Portfolio
18.1.10.3. Profitability by Market Segments
18.1.10.4. Sales Footprint
18.1.10.5. Strategy Overview
18.1.10.5.1. Marketing Strategy
19. Assumptions & Acronyms Used
20. Research Methodology
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Automated Machine Learning Market
