The Business Research Company’s 2026 market reports combine market attractiveness scoring, total addressable market sizing, company benchmarking matrices, interactive Excel dashboards, deeper supply chain intelligence, emerging startup tracking, and granular product-level insights to deliver research that is more actionable and strategically valuable.
Automated Machine Learning (AutoML) Market Revenue Forecast: $3.43 Billion In 2026 Rising To $16.06 Billion In 2030
The market size for automated machine learning (automl) has experienced substantial growth in recent years. This market is projected to expand from $2.34 billion in 2025 to $3.43 billion in 2026, reflecting a compound annual growth rate (CAGR) of 46.5%. Historically, this expansion has been propelled by a shortage of skilled data scientists, the increasing volume of enterprise data, the widespread adoption of cloud computing, a rising demand for faster analytics, and the broadening application of AI across numerous industries.
The automated machine learning (automl) market is projected to experience rapid expansion over the coming years. This market is forecasted to reach $16.06 billion by 2030, exhibiting a compound annual growth rate (CAGR) of 47.0%. This growth during the forecast period is primarily driven by factors such as the increasing adoption by small and medium enterprises, its integration with business intelligence tools, the rise of automated decision-making systems, the demand for real-time analytics, and the broader expansion of AI-driven digital transformation. Key trends anticipated over the same period encompass the simplification of model development, automated feature engineering, the quick deployment of ML models, the democratization of data science, and the emergence of scalable cloud-based AutoML platforms.
Free Sample Report: Automated Machine Learning (AutoML) Market Size, Trends And Forecast To 2030:
Automated Machine Learning (AutoML) Market Growth Catalyst: What Is Creating New Demand?
The increasing demand for sophisticated fraud detection solutions is projected to fuel expansion in the automated machine learning (AutoML) market in the future. Fraud detection itself involves pinpointing and thwarting deceitful actions or conduct within any system or organization. Automated machine learning (AutoML) can be effectively utilized for fraud detection by leveraging its capabilities to process and analyze large volumes of data, recognize recurring patterns, and spot anomalies that may indicate fraudulent activities. For instance, in February 2024, according to Allianz Insurance plc, a Germany-based company offering insurance and asset management services, $95.2 million (£77.4 million) worth of claims fraud were identified in 2023, marking an increase from $86.96 million (£70.7 million) in 2022. Therefore, the growing requirement for advanced fraud detection solutions is a significant driver for the expansion of the automated machine learning (AutoML) market.
Automated Machine Learning (AutoML) Market Segment Overview: Categories Covered In The 2026 Report
The automated machine learning (automl) market covered in this report is segmented –
1) By Offering: Solutions, Services
2) By Deployment: Cloud, On-Premises
3) By Enterprise: Small And Medium Enterprise, Large Enterprise
4) By Application: Data Processing, Feature Engineering, Model Selection, Hyperparameter Optimization And Tuning, Model Assembling, Other Applications
5) By End User: Banking, Financial Services And Insurance (BFSI), Retail And E-Commerce, Healthcare, Manufacturing, Other End Users
Subsegments:
1) By Solutions: Cloud-Based Solutions, On-Premises Solutions, Integrated Development Environments (IDEs)
2) By Services: Consulting Services, Implementation Services, Training And Support Services
How Are Leading Companies Shaping Automated Machine Learning (AutoML) Market Trends?
Leading companies in the automated machine learning (AutoML) market are concentrating on developing innovative solutions, including an AutoML platform for ARM compilers. AutoML for the Arm compiler typically refers to the integration of automated machine learning (AutoML) functionalities with the Arm compiler, which is a compiler designed to generate machine code for Arm processors. As an illustration, in March 2023, TDK Corporation, a Tokyo-based electronic solutions manufacturer, unveiled the launch of ‘Qeexo AutoML’. The Qeexo AutoML platform is specifically designed for lightweight Cortex-M0 to -M4 class processors, offering support for a diverse range of machine learning algorithms. Notably, it excels in delivering ultra-low latency and minimal power consumption. This platform empowers customers to rapidly create and implement machine learning solutions by utilizing sensor data. With an exceptionally small memory footprint, it is ideal for deployment in industrial, IoT, wearables, automotive, mobile, and other resource-constrained environments.
Automated Machine Learning (AutoML) Market Players: Key Companies Shaping Competition
Major companies operating in the automated machine learning (automl) market are 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; H2O.AI Inc.; KNIME; Cognitivescale; Anyscale Inc.; RapidMiner; Squark AI Inc.; Auger.AI; DotData Inc.; BigML Inc.; Valohai; DarwinAI; Aible Inc.; SigOpt; Xpanse AI; Neptune Labs
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Automated Machine Learning (AutoML) Market Regional Performance And Geographic Coverage
North America was the largest region in the automated machine learning (AutoML) market in 2025. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the automated machine learning (automl) market report are Asia-Pacific, South East Asia, Western Europe, Eastern Europe, North America, South America, Middle East, Africa.
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Wasay has over a decade of experience in market research, data modelling, and analytics, with prior experience at GlobalData and Decision Tree Consulting Services. At The Business Research Company , he leads research operations across syndicated studies, customized consulting engagements, and the Global Market Model platform. His professional experience includes supporting organizations such as Boston Consulting Group, KPMG, and Ernst & Young. Wasay holds a degree in Electronics and Communications Engineering, postgraduate management qualifications from International Management Institute Belgium and Indian School of Business and Entrepreneurship, and completed the Integrated Program in Business Analytics from Indian Institute of Management Indore.
