Automated Machine Learning (AutoML) Market is projected to grow at a CAGR of nearly 41.82% between 2024 and 2032

The global automated machine learning (AutoML) market size was worth around USD 2.54 billion in 2023 and is predicted to grow to around USD 58.95 billion by 2032, with a compound annual growth rate (CAGR) of roughly 41.82% between 2024 and 2032.

Automated Machine Learning (AutoML) is a technology that helps users build machine learning models automatically with little or no coding. It simplifies complex tasks such as data preparation, feature selection, model selection, training, and optimization, making artificial intelligence easier to use. The automated machine learning market is growing as businesses use AutoML to save time, reduce costs, and improve decision-making. It is widely used in industries such as banking, healthcare, retail, manufacturing, and telecommunications for applications including fraud detection, sales forecasting, customer analysis, predictive maintenance, and quality control. The increasing adoption of cloud computing, growing demand for AI-powered solutions, and the need for faster data-driven insights are driving the growth of the automated machine learning (AutoML) market worldwide. Growing investments in digital transformation and the rising use of AI by small and medium-sized businesses are further supporting the expansion of the market.

The automated machine learning industry is growing because organizations want faster access to predictive insights, skilled data science talent remains scarce and expensive, and cloud platforms are making powerful automation tools available to almost anyone.

Browse the full “Automated Machine Learning (AutoML) Market By Deployment (On-Premises and Cloud), By Application (Model Selection, Data Processing, Hyperparameter Optimization & Tuning, Feature Engineering, and Others), By Offering (Service and Solution), By Enterprise Size (Large Enterprises and Small & Medium Enterprises), and By Region - Global and Regional Industry Overview, Market Intelligence, Comprehensive Analysis, Historical Data, and Forecasts 2024 - 2032” Report at https://www.zionmarketresearch.com/report/automated-machine-learning-automl-market

Market Growth Factors

Several factors are driving growth in the automated machine learning market.

• Shortage of skilled data scientists: Businesses everywhere struggle to hire enough qualified machine learning experts, pushing them toward AutoML tools that let existing staff build models with minimal coding knowledge.
• Growing volume and complexity of data: As companies collect more information from customers, sensors, and digital platforms, AutoML helps them make sense of that data quickly instead of waiting weeks for manual model building.
• Expanding cloud computing adoption: Cloud-based AutoML platforms give organizations instant access to computing power and pre-built algorithms without heavy upfront infrastructure investment.

Restraints

• Limited transparency in model decisions: Some AutoML systems function like a black box, making it hard for regulators and business leaders to understand exactly how a model reached its conclusion.
• High dependency on data quality: AutoML tools can only perform as well as the data fed into them, and messy or incomplete datasets can produce unreliable predictions despite the automation.

Automated Machine Learning (AutoML) Market: Report Scope

Market Segmentation

The automated machine learning market can be segmented by offering, deployment mode, organization size, application, and region.

Based on offering, the automated machine learning market is segregated into solutions and services. Solutions hold the largest share because businesses prefer ready-to-use platforms that can be deployed quickly, reduce implementation time, improve productivity, and support machine learning projects across multiple departments.

Based on deployment mode, the market is classified into cloud and on-premises. Cloud deployment is growing the fastest since it offers easy scalability, lower maintenance costs, faster implementation, remote accessibility, and greater flexibility compared with traditional in-house infrastructure.

Based on organization size, the market is categorized into large enterprises and small and medium enterprises. Large enterprises currently lead adoption because they have larger IT budgets, greater volumes of business data, and stronger demand for AI-driven automation, while SMEs are rapidly increasing adoption through affordable subscription-based platforms.

Based on application, the automated machine learning industry is divided into fraud detection, predictive maintenance, customer analytics, demand forecasting, and others. Customer analytics holds a significant share because organizations increasingly use AutoML to understand customer behavior, improve personalization, optimize marketing campaigns, and enhance business decision-making.

North America holds the largest share of the automated machine learning market due to the presence of leading technology companies and the early adoption of artificial intelligence across industries. The United States leads the region with strong investments in cloud computing, AI technologies, and digital transformation. Businesses in banking, healthcare, retail, manufacturing, and finance are increasingly using AutoML to improve decision-making and automate data analysis. Canada is also experiencing steady growth as startups, enterprises, and research organizations expand their use of AI-powered solutions. The automated machine learning (AutoML) market in North America benefits from advanced IT infrastructure, high technology spending, and strong government and private sector support for AI innovation. Growing demand for no-code AI platforms and predictive analytics is expected to drive market growth across the region further.

Key Market Players

Leading companies in the global automated machine learning market include:

• IBM Watson Studio
• Google Cloud AutoML
• Neural Designer
• Salesforce Einstein
• Arimo (acquired by Panasonic)
• Domino Data Lab
• Amazon SageMaker Autopilot
• SAP Analytics Cloud
• Microsoft Azure Machine Learning
• Zegami
• RapidMiner
• DataRobot
• AWS SageMaker
• SigOpt
• H2O.ai
• BigML
• NVIDIA
• Knime
• Alteryx
• Tecton.

Recent Developments

• In January 2026, H2O.ai partnered with Certis to industrialize and democratize AI by deploying enterprise AI and AutoML solutions for security and operational applications across Asia.

The global automated machine learning (AutoML) market is segmented as follows:

By Deployment

• On-Premises
• Cloud

By Application

• Model Selection
• Data Processing
• Hyperparameter Optimization & Tuning
• Feature Engineering
• Others

By Offering

• Service
• Solution

By Enterprise Size

• Large Enterprises
• Small & Medium Enterprises

By Region

• North America
  • The U.S.
  • Canada
• Europe
  • France
  • The UK
  • Spain
  • Germany
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Southeast Asia
  • Rest of Asia Pacific
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America
• Middle East & Africa
  • GCC
  • South Africa
  • Rest of Middle East & Africa

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