The Automated Machine Learning (Automl) Market is valued at USD 2.8 billion in 2025 and is projected to grow at a CAGR of 34.1% to reach USD 39.3 billion by 2034.
The automated machine learning (AutoML) market is experiencing rapid growth, driven by the increasing demand for accessible and efficient machine learning solutions. AutoML platforms automate the process of building and deploying machine learning models, reducing the need for specialized expertise. The market encompasses a range of platforms for various applications, including predictive analytics, image recognition, and natural language processing.
Technological advancements in AI, cloud computing, and software development are enhancing the capabilities of AutoML platforms. The integration of user-friendly interfaces and pre-built models is improving accessibility for non-experts. The market is witnessing increased demand for customizable and scalable AutoML solutions.
The competitive landscape is characterized by a mix of cloud service providers, software companies, and specialized AutoML vendors. Strategic partnerships and collaborations are crucial for developing integrated AutoML solutions. The growing focus on democratizing AI and the increasing demand for efficient data analysis are driving market expansion.
Pre-Built Models and Algorithms: Simplifying the process of building machine learning models.
Cloud-Based Platforms: Enhancing scalability and accessibility with cloud solutions.
Customizable Solutions: Adapting AutoML platforms to specific needs.
Automated Feature Engineering: Automating the process of selecting and transforming features.
Democratization of AI: Making machine learning accessible to a wider audience.
Increased Demand for Data Analysis: Automating machine learning to improve data analysis.
Shortage of Machine Learning Experts: Automating machine learning to reduce the need for specialized expertise.
Efficiency and Speed: Automating the process of building and deploying models.
Cloud Adoption: Leveraging cloud computing for scalable AutoML solutions.
Accuracy and Reliability: Ensuring the accuracy and reliability of automated models.
Interpretability and Explainability: Making automated models more transparent and understandable.
Data Quality and Preparation: Ensuring high-quality data for training models.
Customization and Flexibility: Balancing automation with customization and flexibility.
Security and Privacy: Protecting sensitive data used in machine learning.
Macroeconomic indicators, policy frameworks such as carbon pricing and energy security strategies, and evolving consumer behavior are considered in forecasting scenarios. Recent deal flows, partnerships, and technology innovations are incorporated to assess their impact on future market performance.
Regional insights highlight the most promising investment destinations, regulatory landscapes, and evolving partnerships across energy and industrial corridors.
This product will be delivered within 1-3 business days.
The automated machine learning (AutoML) market is experiencing rapid growth, driven by the increasing demand for accessible and efficient machine learning solutions. AutoML platforms automate the process of building and deploying machine learning models, reducing the need for specialized expertise. The market encompasses a range of platforms for various applications, including predictive analytics, image recognition, and natural language processing.
Technological advancements in AI, cloud computing, and software development are enhancing the capabilities of AutoML platforms. The integration of user-friendly interfaces and pre-built models is improving accessibility for non-experts. The market is witnessing increased demand for customizable and scalable AutoML solutions.
The competitive landscape is characterized by a mix of cloud service providers, software companies, and specialized AutoML vendors. Strategic partnerships and collaborations are crucial for developing integrated AutoML solutions. The growing focus on democratizing AI and the increasing demand for efficient data analysis are driving market expansion.
Key Insights: Automated Machine Learning (Automl) Market
User-Friendly Interfaces: Improving accessibility for non-experts.Pre-Built Models and Algorithms: Simplifying the process of building machine learning models.
Cloud-Based Platforms: Enhancing scalability and accessibility with cloud solutions.
Customizable Solutions: Adapting AutoML platforms to specific needs.
Automated Feature Engineering: Automating the process of selecting and transforming features.
Democratization of AI: Making machine learning accessible to a wider audience.
Increased Demand for Data Analysis: Automating machine learning to improve data analysis.
Shortage of Machine Learning Experts: Automating machine learning to reduce the need for specialized expertise.
Efficiency and Speed: Automating the process of building and deploying models.
Cloud Adoption: Leveraging cloud computing for scalable AutoML solutions.
Accuracy and Reliability: Ensuring the accuracy and reliability of automated models.
Interpretability and Explainability: Making automated models more transparent and understandable.
Data Quality and Preparation: Ensuring high-quality data for training models.
Customization and Flexibility: Balancing automation with customization and flexibility.
Security and Privacy: Protecting sensitive data used in machine learning.
Automated Machine Learning (Automl) Market Segmentation
By Offering
- Solutions
- Services
By Deployment
- Cloud
- On-Premises
By Enterprise
- Small and Medium Enterprise
- Large Enterprise
By Application
- Data Processing
- Feature Engineering
- Model Selection
- Hyperparameter Optimization and Tuning
- Model Assembling
- Other Applications
By End User
- Banking
- Financial Services and Insurance (BFSI)
- Retail and E-Commerce
- Healthcare
- Manufacturing
- Other End Users
Key Companies Analysed
- Google LLC
- Microsoft Corporation
- Amazon Web Services Inc.
- International Business Machines Corporation
- Oracle Corporation
- Salesforce Inc.
- Teradata Corporation
- Alteryx
- Altair Engineering Inc.
- EdgeVerve Systems Limited
- TIBCO Software Inc.
- DataRobot Inc.
- Dataiku
- BigPanda.
- H2O.ai Inc.
- KNIME
- Cognitivescale
- Anyscale Inc.
- RapidMiner
- Squark AI Inc.
- Auger.AI
- DotData Inc.
- BigML Inc.
- Valohai
- DarwinAI
- Aible Inc.
- SigOpt
- Zerion
- Xpanse AI
- Neptune Labs
Automated Machine Learning (Automl) Market Analytics
The report employs rigorous tools, including Porter’s Five Forces, value chain mapping, and scenario-based modeling, to assess supply-demand dynamics. Cross-sector influences from parent, derived, and substitute markets are evaluated to identify risks and opportunities. Trade and pricing analytics provide an up-to-date view of international flows, including leading exporters, importers, and regional price trends.Macroeconomic indicators, policy frameworks such as carbon pricing and energy security strategies, and evolving consumer behavior are considered in forecasting scenarios. Recent deal flows, partnerships, and technology innovations are incorporated to assess their impact on future market performance.
Automated Machine Learning (Automl) Market Competitive Intelligence
The competitive landscape is mapped through proprietary frameworks, profiling leading companies with details on business models, product portfolios, financial performance, and strategic initiatives. Key developments such as mergers & acquisitions, technology collaborations, investment inflows, and regional expansions are analyzed for their competitive impact. The report also identifies emerging players and innovative startups contributing to market disruption.Regional insights highlight the most promising investment destinations, regulatory landscapes, and evolving partnerships across energy and industrial corridors.
Countries Covered
- North America - Automated Machine Learning (Automl) market data and outlook to 2034
- United States
- Canada
- Mexico
- Europe - Automated Machine Learning (Automl) market data and outlook to 2034
- Germany
- United Kingdom
- France
- Italy
- Spain
- BeNeLux
- Russia
- Sweden
- Asia-Pacific - Automated Machine Learning (Automl) market data and outlook to 2034
- China
- Japan
- India
- South Korea
- Australia
- Indonesia
- Malaysia
- Vietnam
- Middle East and Africa - Automated Machine Learning (Automl) market data and outlook to 2034
- Saudi Arabia
- South Africa
- Iran
- UAE
- Egypt
- South and Central America - Automated Machine Learning (Automl) market data and outlook to 2034
- Brazil
- Argentina
- Chile
- Peru
Research Methodology
This study combines primary inputs from industry experts across the Automated Machine Learning (Automl) value chain with secondary data from associations, government publications, trade databases, and company disclosures. Proprietary modeling techniques, including data triangulation, statistical correlation, and scenario planning, are applied to deliver reliable market sizing and forecasting.Key Questions Addressed
- What is the current and forecast market size of the Automated Machine Learning (Automl) industry at global, regional, and country levels?
- Which types, applications, and technologies present the highest growth potential?
- How are supply chains adapting to geopolitical and economic shocks?
- What role do policy frameworks, trade flows, and sustainability targets play in shaping demand?
- Who are the leading players, and how are their strategies evolving in the face of global uncertainty?
- Which regional “hotspots” and customer segments will outpace the market, and what go-to-market and partnership models best support entry and expansion?
- Where are the most investable opportunities - across technology roadmaps, sustainability-linked innovation, and M&A - and what is the best segment to invest over the next 3-5 years?
Your Key Takeaways from the Automated Machine Learning (Automl) Market Report
- Global Automated Machine Learning (Automl) market size and growth projections (CAGR), 2024-2034
- Impact of Russia-Ukraine, Israel-Palestine, and Hamas conflicts on Automated Machine Learning (Automl) trade, costs, and supply chains
- Automated Machine Learning (Automl) market size, share, and outlook across 5 regions and 27 countries, 2023-2034
- Automated Machine Learning (Automl) market size, CAGR, and market share of key products, applications, and end-user verticals, 2023-2034
- Short- and long-term Automated Machine Learning (Automl) market trends, drivers, restraints, and opportunities
- Porter’s Five Forces analysis, technological developments, and Automated Machine Learning (Automl) supply chain analysis
- Automated Machine Learning (Automl) trade analysis, Automated Machine Learning (Automl) market price analysis, and Automated Machine Learning (Automl) supply/demand dynamics
- Profiles of 5 leading companies - overview, key strategies, financials, and products
- Latest Automated Machine Learning (Automl) market news and developments
Additional Support
With the purchase of this report, you will receive:- An updated PDF report and an MS Excel data workbook containing all market tables and figures for easy analysis.
- 7-day post-sale analyst support for clarifications and in-scope supplementary data, ensuring the deliverable aligns precisely with your requirements.
- Complimentary report update to incorporate the latest available data and the impact of recent market developments.
This product will be delivered within 1-3 business days.
Table of Contents
1. Table of Contents
2. Global Automated Machine Learning (Automl) Market Summary, 2025
3. Automated Machine Learning (Automl) Market Insights, 2024-2034
4. Automated Machine Learning (Automl) Market Analytics
5. Global Automated Machine Learning (Automl) Market Statistics - Industry Revenue, Market Share, Growth Trends and Forecast by segments, to 2034
6. Asia Pacific Automated Machine Learning (Automl) Industry Statistics - Market Size, Share, Competition and Outlook
7. Europe Automated Machine Learning (Automl) Market Data, Penetration, and Business Prospects to 2034
8. North America Automated Machine Learning (Automl) Market Size, Growth Trends, and Future Prospects to 2034
9. South and Central America Automated Machine Learning (Automl) Market Drivers, Challenges, and Future Prospects
10. Middle East Africa Automated Machine Learning (Automl) Market Outlook and Growth Prospects
11. Automated Machine Learning (Automl) Market Structure and Competitive Landscape
12 Appendix
Companies Mentioned
- Google LLC
- Microsoft Corporation
- Amazon Web Services Inc.
- International Business Machines Corporation
- Oracle Corporation
- Salesforce Inc.
- Teradata Corporation
- Alteryx
- Altair Engineering Inc.
- EdgeVerve Systems Limited
- TIBCO Software Inc.
- DataRobot Inc.
- Dataiku
- BigPanda.
- H2O.ai Inc.
- KNIME
- Cognitivescale
- Anyscale Inc.
- RapidMiner
- Squark AI Inc.
- Auger.AI
- DotData Inc.
- BigML Inc.
- Valohai
- DarwinAI
- Aible Inc.
- SigOpt
- Zerion
- Xpanse AI
- Neptune Labs
Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 160 |
| Published | October 2025 |
| Forecast Period | 2025 - 2034 |
| Estimated Market Value ( USD | $ 2.8 Billion |
| Forecasted Market Value ( USD | $ 39.3 Billion |
| Compound Annual Growth Rate | 34.1% |
| Regions Covered | Global |
| No. of Companies Mentioned | 30 |
