Automated Machine Learning (AUTOML) Market Size, Share, Opportunities And Trends By Provider (Open Source, Startups, Tech Giants), By Application (Fraud Detection, AML Detection, Pricing, Marketing and Sales Management, Others), And By Geography - Forecasts From 2025 To 2030

Description

Automated Machine Learning Market Size:

Automated Machine Learning (AUTOML) Market, at a 42.37% CAGR, is projected to increase from USD 1.933 billion in 2025 to USD 11.306 billion by 2030.

Key Market Highlights:

• The market for autoML is expanding quickly due to rising demand for low-code/no-code solutions and AI democratization.
• The market is expanding due to the growing use of cloud-based machine learning platforms.
• AutoML tools are being used by SMEs increasingly to lessen their reliance on data science specialists.
• The scalability and performance of models are being improved through the integration of AutoML with big data and analytics platforms.
• Major end users implementing AutoML for automation and predictive analytics include the BFSI, healthcare, and retail industries.   

Automated Machine Learning (AutoML) is a process of using Artificial Intelligence (AI) algorithms to automate the process of building, optimizing, and deploying machine learning models. It is a technology that enables businesses to build predictive models with minimal human intervention automatically.

The rising demand for autoML products can be attributed to their resourcefulness and usefulness in creating accurate models to make better predictions about customers, products, or other important business metrics quickly and easily for businesses that do not have proper access to data scientists or have limited expertise in machine learning. AutoML works by automating the selection of the best ML algorithms for a given task by simultaneously designing the feature engineering and hyperparameter tuning required to optimize model performance. In addition, it can automate the deployment and scaling of models to support production use cases.

The AutoML market growth is expected to be driven by the need for machine learning solutions with enhanced speed, efficiency, and accuracy, combined with the existing shortage of data science experts and the increasing adoption of AI and cloud services across industries.  

Key players working in the market include IBM, Microsoft Corporation, Amazon Web Services, Oracle, Alphabet Inc. (Google), Databricks, Qlik, Akkio Inc., and Obviously AI, Inc..

Automated Machine Learning (AUTOML) Market Trends

Rising Demand for AI Democratization and Low-Code Solutions

The expansion of the AutoML market is highly dependent on the need for AI technology to be made accessible to everyone. In the past, the creation of machine learning models had to be done by highly skilled data scientists who had a thorough knowledge of programming and statistics. AutoML software tools make this task less complicated by providing intuitive, low-code or no-code interfaces that users with no technical background can utilize to construct, train, and deploy models in an effective manner. Such a democratization of AI enables companies with any kind of capital to use AI as an instrument in their business practice without the necessity of many specialized talents, thus, opening the potential user group of AutoML tools to a great extent.   

Automated Machine Learning (AUTOML) Market Dynamics

Market Drivers

Increasing Need for Data Analysis and Prediction by Companies

The skyrocketing amount of data produced and gathered by businesses is augmenting the need for data analysis and forecasting models. Consequently, the autoML market is expanding with the rise of such solutions that allow companies to handle this data quickly, efficiently, and accurately, thus, they are enabled to extract valuable insights from their data. To illustrate, PayPal confirmed that the accuracy of its fraud detection model went up from 89% to 94.7% by using H2O.ai's AutoML tool only. Besides that, the sales prediction model of Lenovo became 7.5% more accurate after employing the autoML software by DataRobot. Moreover, California Design Den, a company that provides bedding solutions, has reduced its inventory carryover by nearly half using the autoML tool offered by Google.  

Increasing Adoption of Cloud-Based Machine Learning Platforms

One of the major factors that has led to the massive use of AutoML is the move to cloud computing. The cloud-based AutoML systems can offer enterprises unlimited computing power, storage, and integration with their data pipelines. Thus, they can run heavy models on a costly basis for a short period of time compared to traditional data centers. Besides that, the world's biggest cloud service providers, i.e., AWS, Microsoft Azure, and Google Cloud, are embedding AutoML features in their suites, thereby simplifying and speeding up the process of AI-driven solutions adoption for enterprises.    

Market Restraints

High Implementation and Integration Costs    

Although AutoML platforms are said to be efficient and scalable, the costs of their first installation, integration, and customization can be very high, particularly for small and medium-sized enterprises (SMEs). To use AutoML tools, one may have to spend money on setting up cloud infrastructure, data pipelines, and API integrations with the current systems. Besides that, companies might have to pay for staff retraining or for the hiring of consultants to manage the technology, thus adding to the total cost of ownership. These monetary obstacles may restrict the use of such technologies in sectors that are sensitive to costs, as well as in less developed areas.

Limited Customization of AutoML Solutions  

AutoML solutions are expensive, which could restrain their adoption by various small and medium-sized firms and businesses, as they need to weigh the benefits of using these solutions against the cost to ensure that the return on investment is sufficient. Further, these solutions are highly limited in customization for automating the process of building machine learning models. This limits their adoption by businesses that require effectively customized AutoML solutions, since integrating non-customized AutoML solutions with existing business applications and workflows can be challenging.    

Market Opportunities

Growing Need for Accelerated Model Development and Deployment  

In the fast-moving digital world of today, companies are looking for quick data-based insights to be able to decide instantly. The development of traditional machine learning may take a long time, and it requires a lot of resources. AutoML shortens the model development cycle, which is possible by speeding up the stages of experimentation, deployment, and iteration. Such effectiveness can be of great value in those types of firms, for instance, in finance, health, and e-commerce, where the use of predictive analytics and automation within a short period of time is a prerequisite for keeping the competitive advantage.   

Automated Machine Learning (AUTOML) Market Segmentation Analysis

By Provider

Based on the provider, the market is classified into open source, startups, and tech giants.

Tech giants like Google, Amazon, and Microsoft have invested heavily in AutoML solutions by recognizing the growing demand for automated ML solutions at the initial stage. For instance, Microsoft Azure offers a cloud-based AutoML solution that enables businesses to build custom machine learning models without requiring extensive technical expertise to automate several tasks involved in building ML models, including feature engineering, algorithm selection, and hyperparameter tuning. Furthermore, the increase in the efficiency of the software platforms offered by these companies is increasing the consumption of their autoML platforms.

By Application

Based on application, the market is classified into fraud detection, AML detection, pricing, marketing and sales management, and others.

AutoML is extensively adopted to build predictive models for fraud detection in different industries, such as the BFSI and e-commerce, through data preparation, model selection, hyperparameter tuning, and ensemble methods. It performs data cleaning and preprocessing tasks such as data imputation, scaling, and feature engineering, ensuring that the data used for fraud detection is accurate and consistent. In addition, it can tune the hyperparameters of ML models to optimize their performance on a given dataset to ensure that the fraud detection models are robust and can generalize appropriately to new data.

The rising incidents of online fraud in various e-commerce sites and companies operating in the BFSI are expected to increase the market share of this sector over the forecast period, as it is generating a high demand for fraud detection solutions and models. For instance, fraud incidents among major banking companies in India in March 2021 amounted to Rs 4.92 trillion. It increased by Rs. 36,342 crores during September 2022, as per research conducted by Indiaforensic. In addition, Worldline SA, a leading company offering transaction and payment services, estimated that payment fraud created a loss of 3.6% of the total sales made by e-commerce vendors in 2022.     

Automated Machine Learning (AUTOML) Market Regional Analysis

By region, the market is segmented into North America, South America, the Middle East and Africa, and Asia-Pacific.

North America

North America is the primary contributor to the global AutoML market. This is mainly due to the region's early adoption of sophisticated AI technologies, the presence of numerous influential market players, and an excellently developed IT infrastructure. The US is the major contributor to the area due to its well-functioning innovation ecosystem, high investment in R&D, and the presence of tech giants such as Google, Microsoft, IBM, and Amazon Web Services, which are the leading providers of cloud-integrated AutoML platforms. BFSI, healthcare, and retail sectors, among others, are utilizing AutoML for predictive analytics, fraud detection, and customer personalization in North America. The region's advanced digital ecosystem, together with the rising enterprise demand for AI-driven decision-making, is still positioning North America as a global leader in the AutoML market.

Europe

Europe plays a major role in the global AutoML market, which is mainly driven by the rapid digital transformation of various industries and the growing number of government initiatives aimed at promoting AI innovation. The adoption rate in the United Kingdom, Germany, and France is quite high as these countries have well-established AI research-based frameworks, many analytics firms, and supportive regulations for AI ethics and transparency. European companies are progressively deploying AutoML-powered solutions for a variety of use cases in manufacturing process optimization, financial forecasting, and healthcare diagnostics. On the other hand, stringent data protection laws like the General Data Protection Regulation (GDPR) make data handling quite challenging, thus affecting the strategies for deployment. So far, Europe is projected to continue its growth at a moderate pace because of its commitment to the use of explainable and ethical AI models, notwithstanding the presence of regulatory obstacles.

Asia-Pacific

Asia-Pacific is the biggest market of AutoML with a very rapid growth, with the pace towards digitization being very fast, cloud adoption skyrocketing, and AI initiatives led by governments. China, India, Japan, and South Korea, among others, are investing heavily in AI research and automation to drive the industrial revolution. The Chinese AI development got a boost from the programs sponsored by the government and the availability of big data, while India can only get more demand for AutoML solutions from its startup ecosystem and digital economy, which are both growing fast. Furthermore, the APAC sectors like e-commerce, manufacturing, and telecommunications are on the move, using AutoML for predictive analytics, customer insights, and process automation. With the region's growing IT infrastructure and concentration on AI upskilling, its market expansion will be at a very high speed.

South America

The South American Automated Machine Learning (AutoML) market is geographically at a very interesting stage. With the regional organizations slowly but surely committing to digital transformation and data-driven decision-making, the market is moving towards the next phase of development. On the solid adoption foundations made possible via cloud computing explosion, fintech and AI techniques-driven automation, Brazil, Mexico, Chile, and Argentina are the countries that are currently out in front, creating a vibrant regional ecosystem. Among them, Brazil is particularly noteworthy for its becoming a local hub of the AI-driven innovation trend, thanks to its booming startup scene, the powerful fintech sector, and government programs fostering innovation and AI education. Banking and financial services, retail, telecommunications, and manufacturing are top of the list industries where AutoML software usage has become commonplace to realize goals like predictive analytics, fraud detection, customer segmentation, and operational optimization.

The Middle East and Africa

AutoML solutions are slowly being accepted in the Middle East and Africa region, which is basically happening in the Gulf Cooperation Council (GCC) countries like the UAE, Saudi Arabia, and Qatar. Governments in these countries are going all out to implement AI-driven digital transformation programs that are in line with the national visions, such as Saudi Vision 2030 and the UAE’s National AI Strategy 2031. The extensive application of AI in banking, oil & gas, and smart city projects is making AutoML the next logical step in these industries.   

Automated Machine Learning (AUTOML) Market Competitive Landscape

Key Industry Players

The top companies leading the charge in the AutoML market are IBM, Microsoft Corporation, Amazon Web Services (AWS), Oracle, Alphabet Inc. (Google), Databricks, Qlik, Akkio Inc., and Obviously AI, Inc. By adding AutoML features to their AI and cloud ecosystems, these companies give their customers the power to create and install models with little or no coding skills. For instance, big tech companies like IBM, Microsoft, and Google provide broad AutoML platforms that are supported by cloud scalability and AI automation, whereas startups like Akkio and Obviously AI concentrate on easy-to-use, no-code solutions that are mainly for SMEs.     

List of Key Company Profiled

• IBM
• Microsoft Corporation
• Amazon Web Services
• Oracle
• Alphabet Inc. (Google)
• Databricks
• Qlik
• Akkio Inc.
• Obviously AI, Inc.

 Automated Machine Learning (AUTOML) Market Key Developments

• Research and Development: In June 2025, the SAP HANA Cloud Predictive Analysis Library (PAL) and the Automated Predictive Library (APL) were released along with several new embedded Machine Learning/AI features with the SAP HANA Cloud 2025 Q2 release.
• Collaboration: In March 2025, Macquarie University and Fujitsu collaborated to address the severe lack of machine learning engineers. Students will have access to Fujitsu's AutoML technology through a new micro-credentials course, which will enable them to produce AI models more quickly.  

• Product Launch: In January 2025, regardless of technical expertise, machine learning is now more accessible due to the introduction of low-code AutoML in Fabric Data Science by Microsoft. Currently in preview, this new low-code AutoML experience expands upon the code-first version that was revealed in April of last year.

The Auto ML Market is segmented and analyzed as follows:

• AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY OFFERINGS
  • Solutions
  • Services
• AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY DEPLOYMENT
  • Cloud
  • On-Premise
• AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY ENTERPRISE SIZE
  • Small & Medium Enterprise (SMEs)
  • Large Enterprise
• AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY APPLICATION
  • Fraud Detection
  • AML Detection
  • Marketing & Sales Management
  • Data Processing
  • Feature Engineering
  • Others
• AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY END-USER
  • BFSI
  • Healthcare
  • Retail & E-Commerce
  • Manufacturing
  • IT & Telecommunication
  • Others
• AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY GEOGRAPHY
  • North America
    • USA
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • Germany
    • France
    • United Kingdom
    • Spain
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Israel
    • Others
  • Asia Pacific
    • China
    • India
    • Japan
    • South Korea
    • Others

Table Of Contents

1. EXECUTIVE SUMMARY 

2. MARKET SNAPSHOT

2.1. Market Overview

2.2. Market Definition

2.3. Scope of the Study

2.4. Market Segmentation

3. BUSINESS LANDSCAPE 

3.1. Market Drivers

3.2. Market Restraints

3.3. Market Opportunities 

3.4. Porter’s Five Forces Analysis

3.5. Industry Value Chain Analysis

3.6. Policies and Regulations 

3.7. Strategic Recommendations 

4. TECHNOLOGICAL OUTLOOK 

5. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY PROVIDER 

5.1. Introduction

5.2. Open Source

5.3. Startups

5.4. Tech Giants

6. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY DEPLOYMENT 

6.1. Introduction

6.2. Cloud-Based 

6.3. On-Premises

7. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY APPLICATION 

7.1. Introduction

7.2. Fraud Detection

7.3. AML Detection

7.4. Pricing

7.5. Marketing and Sales Management

7.6. Others

8. AUTOMATED MACHINE LEARNING (AUTOML) MARKET BY GEOGRAPHY 

8.1. Introduction

8.2. North America

8.2.1. By Provider

8.2.2. By Application

8.2.3. By Country

8.2.3.1. United States

8.2.3.2. Canada

8.2.3.3. Mexico

8.3. South America

8.3.1. By Provider

8.3.2. By Application 

8.3.3. By Country

8.3.3.1. Brazil 

8.3.3.2. Argentina

8.3.3.3. Others

8.4. Europe

8.4.1. By Provider

8.4.2. By Application 

8.4.3. By Country

8.4.3.1. United Kingdom

8.4.3.2. Germany

8.4.3.3. France

8.4.3.4. Spain

8.4.3.5. Others

8.5. Middle East & Africa

8.5.1. By Provider

8.5.2. By Application  

8.5.3. By Country

8.5.3.1. Saudi Arabia

8.5.3.2. UAE

8.5.3.3. Israel

8.5.3.4. Others

8.6. Asia Pacific

8.6.1. By Provider

8.6.2. By Application    

8.6.3. By Country

8.6.3.1. Japan

8.6.3.2. China

8.6.3.3. India

8.6.3.4. South Korea

8.6.3.5. Indonesia

8.6.3.6. Thailand

8.6.3.7. Others 

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. IBM

10.2. Microsoft Corporation

10.3. Amazon Web Services

10.4. Oracle

10.5. Alphabet Inc. (Google)

10.6. Databricks

10.7. Qlik

10.8. Akkio Inc.

10.9. Obviously AI, Inc.  

11. RESEARCH METHODOLOGY 

LIST OF FIGURES

LIST OF TABLES   

Companies Profiled

IBM

Microsoft Corporation

Amazon Web Services

Oracle

Alphabet Inc. (Google)

Databricks

Qlik

Akkio Inc.

Obviously AI, Inc.  

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