Global Automated Machine Learning (AutoML) Market Size Study & Forecast, by Offering, Application, Vertical and Regional Forecasts 2025-2035

Description

The Global Automated Machine Learning (AutoML) Market is valued at approximately USD 1.45 billion in 2024 and is expected to expand at an exponential CAGR of more than 44.60% over the forecast period 2025-2035. As industries strive to scale AI-powered insights without being encumbered by the limitations of human-intensive data science, AutoML has surfaced as a transformative technology, dramatically streamlining the end-to-end machine learning lifecycle. By abstracting the complexity of model building-from preprocessing to algorithm selection, training, hyperparameter tuning, and deployment-AutoML democratizes AI, enabling even non-experts to operationalize data at scale. This paradigm shift has catalyzed adoption across a swath of sectors, fueling the demand for customizable, scalable, and low-code/no-code machine learning platforms.

The growth trajectory of the AutoML market is propelled by an increasing need for predictive analytics, rapid digitalization, and the rising pressure on enterprises to reduce time-to-value in AI projects. Businesses across finance, retail, manufacturing, and healthcare are embracing AutoML solutions to harness data-driven decision-making with precision and speed. The technology's capability to automate repetitive, technical processes and generate high-performing models using minimal manual intervention not only reduces overhead but enhances innovation cycles. Meanwhile, advancements in explainable AI and integration with cloud-native environments further reinforce the credibility and usability of AutoML platforms. Features such as feature engineering, model ensembling, and adaptive learning have become critical enablers for institutions seeking AI scalability with reduced dependency on scarce data science talent.

Regionally, North America is projected to dominate the global AutoML market in 2025, primarily due to its early adoption of AI, concentration of cloud infrastructure, and presence of tech giants leading the AutoML innovation wave. The United States, home to several AI unicorns and prominent cloud service providers, represents the epicenter of deployment across industries. On the other hand, Asia Pacific is poised for the fastest growth during the forecast period, attributed to increasing digitization efforts, expanding AI policy frameworks, and rising startup ecosystems in countries like China, India, Japan, and South Korea. Meanwhile, Europe's growth is being bolstered by strong R&D initiatives, growing emphasis on AI ethics and transparency, and supportive funding programs from the European Commission.

Major market player included in this report are:

Google LLC
Amazon Web Services
Microsoft Corporation
DataRobot Inc.
IBM Corporation
H2O.ai
Salesforce Inc.
RapidMiner Inc.
BigML Inc.
dotData Inc.
SAS Institute Inc.
KNIME AG
TIBCO Software Inc.
Baidu Inc.
Pecan AI

Global Automated Machine Learning (AutoML) Market Report Scope:

Historical Data - 2023, 2024
Base Year for Estimation - 2024
Forecast period - 2025-2035
Report Coverage - Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
Regional Scope - North America; Europe; Asia Pacific; Latin America; Middle East & Africa
Customization Scope - Free report customization (equivalent up to 8 analysts' working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values for the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within the countries involved in the study. The report also provides detailed information about crucial aspects, such as driving factors and challenges, which will define the future growth of the market. Additionally, it incorporates potential opportunities in micro-markets for stakeholders to invest, along with a detailed analysis of the competitive landscape and product offerings of key players. The detailed segments and sub-segments of the market are explained below:

By Offering:

Solutions
Services

By Application:

Data Processing
Model Selection
Hyperparameter Optimization & Tuning
Feature Engineering
Model Ensembling

By Vertical:

BFSI
Retail & E-Commerce
Healthcare & Life Sciences
Manufacturing
Government & Public Sector
Energy & Utilities
Transportation & Logistics
Others

By Region:

North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE
Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Middle East & Africa
UAE
Saudi Arabia
South Africa
Rest of Middle East & Africa

Key Takeaways:

Market Estimates & Forecast for 10 years from 2025 to 2035.
Annualized revenues and regional level analysis for each market segment.
Detailed analysis of geographical landscape with Country level analysis of major regions.
Competitive landscape with information on major players in the market.
Analysis of key business strategies and recommendations on future market approach.
Analysis of competitive structure of the market.
Demand side and supply side analysis of the market.

Chapter 1. Global Automated Machine Learning (AutoML) Market Report Scope & Methodology

1.1. Research Objective
1.2. Research Methodology
- 1.2.1. Forecast Model
- 1.2.2. Desk Research
- 1.2.3. Top-Down and Bottom-Up Approach
1.3. Research Attributes
1.4. Scope of the Study
- 1.4.1. Market Definition
- 1.4.2. Market Segmentation
1.5. Research Assumption
- 1.5.1. Inclusion & Exclusion
- 1.5.2. Limitations
- 1.5.3. Years Considered for the Study

Chapter 2. Executive Summary

2.1. CEO/CXO Standpoint
2.2. Strategic Insights
2.3. ESG Analysis
2.4. Key Findings

Chapter 3. Global AutoML Market Forces Analysis

3.1. Market Forces Shaping the Global AutoML Market (2024-2035)
3.2. Drivers
- 3.2.1. Rising Demand for Predictive Analytics and Rapid Digitalization
- 3.2.2. Need to Reduce Time-to-Value in AI Deployments
3.3. Restraints
- 3.3.1. Data Privacy, Security and Regulatory Compliance Challenges
- 3.3.2. High Implementation Costs and Technical Integration Complexity
3.4. Opportunities
- 3.4.1. Expansion into Emerging Markets and Industry Verticals
- 3.4.2. Growth of Explainable AI and Cloud-Native Integrations

Chapter 4. Global AutoML Industry Analysis

4.1. Porter's Five Forces Model
- 4.1.1. Bargaining Power of Buyers
- 4.1.2. Bargaining Power of Suppliers
- 4.1.3. Threat of New Entrants
- 4.1.4. Threat of Substitutes
- 4.1.5. Competitive Rivalry
4.2. Porter's Five Forces Forecast Model (2024-2035)
4.3. PESTEL Analysis
- 4.3.1. Political
- 4.3.2. Economic
- 4.3.3. Social
- 4.3.4. Technological
- 4.3.5. Environmental
- 4.3.6. Legal
4.4. Top Investment Opportunities
4.5. Top Winning Strategies (2025)
4.6. Market Share Analysis (2024-2025)
4.7. Global Pricing Analysis and Trends (2025)
4.8. Analyst Recommendations & Conclusion

Chapter 5. Global AutoML Market Size & Forecasts by Offering 2025-2035

5.1. Market Overview
5.2. Solutions
- 5.2.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 5.2.2. Market Size Analysis, by Region, 2025-2035
5.3. Services
- 5.3.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 5.3.2. Market Size Analysis, by Region, 2025-2035

Chapter 6. Global AutoML Market Size & Forecasts by Application 2025-2035

6.1. Market Overview
6.2. Data Processing
- 6.2.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 6.2.2. Market Size Analysis, by Region, 2025-2035
6.3. Model Selection
- 6.3.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 6.3.2. Market Size Analysis, by Region, 2025-2035
6.4. Hyperparameter Optimization & Tuning
- 6.4.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 6.4.2. Market Size Analysis, by Region, 2025-2035
6.5. Feature Engineering
- 6.5.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 6.5.2. Market Size Analysis, by Region, 2025-2035
6.6. Model Ensembling
- 6.6.1. Top Countries Breakdown Estimates & Forecasts, 2024-2035
- 6.6.2. Market Size Analysis, by Region, 2025-2035

Chapter 7. Global AutoML Market Size & Forecasts by Region 2025-2035

7.1. Global Market, Regional Snapshot
7.2. Top Leading & Emerging Countries
7.3. North America AutoML Market
- 7.3.1. U.S. AutoML Market
  - 7.3.1.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.3.1.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.3.2. Canada AutoML Market
  - 7.3.2.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.3.2.2. Application Breakdown Size & Forecasts, 2025-2035
7.4. Europe AutoML Market
- 7.4.1. UK AutoML Market
  - 7.4.1.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.4.1.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.4.2. Germany AutoML Market
  - 7.4.2.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.4.2.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.4.3. France AutoML Market
  - 7.4.3.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.4.3.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.4.4. Spain AutoML Market
  - 7.4.4.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.4.4.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.4.5. Italy AutoML Market
  - 7.4.5.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.4.5.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.4.6. Rest of Europe AutoML Market
  - 7.4.6.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.4.6.2. Application Breakdown Size & Forecasts, 2025-2035
7.5. Asia Pacific AutoML Market
- 7.5.1. China AutoML Market
  - 7.5.1.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.5.1.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.5.2. India AutoML Market
  - 7.5.2.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.5.2.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.5.3. Japan AutoML Market
  - 7.5.3.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.5.3.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.5.4. Australia AutoML Market
  - 7.5.4.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.5.4.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.5.5. South Korea AutoML Market
  - 7.5.5.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.5.5.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.5.6. Rest of APAC AutoML Market
  - 7.5.6.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.5.6.2. Application Breakdown Size & Forecasts, 2025-2035
7.6. Latin America AutoML Market
- 7.6.1. Brazil AutoML Market
  - 7.6.1.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.6.1.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.6.2. Mexico AutoML Market
  - 7.6.2.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.6.2.2. Application Breakdown Size & Forecasts, 2025-2035
7.7. Middle East & Africa AutoML Market
- 7.7.1. UAE AutoML Market
  - 7.7.1.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.7.1.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.7.2. Saudi Arabia AutoML Market
  - 7.7.2.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.7.2.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.7.3. South Africa AutoML Market
  - 7.7.3.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.7.3.2. Application Breakdown Size & Forecasts, 2025-2035
- 7.7.4. Rest of Middle East & Africa AutoML Market
  - 7.7.4.1. Offering Breakdown Size & Forecasts, 2025-2035
  - 7.7.4.2. Application Breakdown Size & Forecasts, 2025-2035

Chapter 8. Competitive Intelligence

8.1. Top Market Strategies
8.2. Google LLC
- 8.2.1. Company Overview
- 8.2.2. Key Executives
- 8.2.3. Company Snapshot
- 8.2.4. Financial Performance (Subject to Data Availability)
- 8.2.5. Product/Services Portfolio
- 8.2.6. Recent Developments
- 8.2.7. Market Strategies
- 8.2.8. SWOT Analysis
8.3. Amazon Web Services
8.4. Microsoft Corporation
8.5. DataRobot Inc.
8.6. IBM Corporation
8.7. H2O.ai
8.8. Salesforce Inc.
8.9. RapidMiner Inc.
8.10. BigML Inc.
8.11. dotData Inc.
8.12. SAS Institute Inc.
8.13. KNIME AG
8.14. TIBCO Software Inc.
8.15. Baidu Inc.
8.16. Pecan AI