Automated Machine Learning (AutoML) Market Report 2026

The automated machine learning (automl) market size has grown exponentially in recent years. It will grow from $2.34 billion in 2025 to $3.43 billion in 2026 at a compound annual growth rate (CAGR) of 46.5%. The growth in the historic period can be attributed to shortage of skilled data scientists, growth of enterprise data volumes, adoption of cloud computing, demand for faster analytics, expansion of AI applications across industries.

The automated machine learning (automl) market size is expected to see exponential growth in the next few years. It will grow to $16.06 billion in 2030 at a compound annual growth rate (CAGR) of 47%. The growth in the forecast period can be attributed to increasing adoption by small and medium enterprises, integration with business intelligence tools, growth of automated decision-making systems, demand for real-time analytics, expansion of ai-driven digital transformation. Major trends in the forecast period include simplification of model development, automated feature engineering, rapid deployment of ml models, democratization of data science, scalable cloud-based automl platforms.

The increasing demand for advanced fraud detection solutions is anticipated to drive the growth of the automated machine learning (AutoML) market in the future. Fraud detection refers to the process of identifying and preventing fraudulent activities or behaviors within a system or organization. Automated machine learning (AutoML) can assist in fraud detection by utilizing its ability to process and analyze large amounts of data, recognize patterns, and identify anomalies that may suggest fraudulent activities. For example, in February 2024, Allianz Insurance plc, a Germany-based company providing insurance and asset management services, reported that $95.2 million (£77.4 million) in claims fraud was detected in 2023, an increase from $86.96 million (£70.7 million) in 2022. Thus, the rising demand for advanced fraud detection solutions is propelling the growth of the automated machine learning (AutoML) market.

Major players in the AutoML market are dedicated to developing innovative solutions, such as an AutoML platform for Arm compilers. AutoML for Arm compiler involves integrating AutoML capabilities with the Arm compiler, which generates machine code for Arm processors. In March 2023, TDK Corporation, a Tokyo-based electronic solutions manufacturer, introduced the ‘Qeexo AutoML’ platform tailored for lightweight Cortex-M0 to -M4 class processors. This platform supports various machine learning algorithms, excelling in ultra-low latency and power consumption. Qeexo AutoML empowers users to rapidly create and implement machine learning solutions using sensor data, making it ideal for deployment in resource-constrained environments such as industrial, IoT, wearables, automotive, and mobile.

In May 2023, Infineon Technologies AG, a Germany-based semiconductor manufacturer, acquired Imagimob AB for an undisclosed sum. This acquisition enables Infineon Technologies to bolster its position in the expanding market for embedded AI solutions and tiny machine learning, improving its ability to provide advanced functionalities and energy-efficient control in IoT applications. Imagimob AB is a Sweden-based company focused on edge AI and tinyML, aimed at facilitating the intelligent products of the future.

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.

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. The countries covered in the automated machine learning (automl) market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Taiwan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Tariffs have had a limited direct impact on the automl market due to its strong software-centric nature. However, indirect effects have arisen from increased costs of imported servers and computing hardware used in on-premise deployments. North america and asia-pacific regions have experienced moderate infrastructure cost pressures. Higher tariffs have encouraged migration toward cloud-based automl solutions. This shift has reduced hardware dependency and accelerated scalable software adoption.

The automated machine learning (automl) market research report is one of a series of new reports that provides automated machine learning (automl) market statistics, including automated machine learning (automl) industry global market size, regional shares, competitors with a automated machine learning (automl) market share, detailed automated machine learning (automl) market segments, market trends and opportunities, and any further data you may need to thrive in the automated machine learning (automl) industry. This automated machine learning (automl) market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Automated machine learning (AutoML) is the application of machine learning to practical problems, automating the selection, composition, and parameterization of machine learning models. AutoML streamlines the machine learning process, making it more user-friendly and often yielding faster and more accurate outputs compared to manually coded algorithms.

The primary offerings in automated machine learning (AutoML) include solutions and services. Solutions involve the implementation of software tools to address specific organizational issues. Automated machine learning solutions enable business users to easily adopt machine learning, allowing data scientists to focus on more complex challenges. These solutions can be deployed in various settings, such as cloud and on-premises, catering to both small and medium enterprises as well as large enterprises. They find applications in data processing, feature engineering, model selection, hyperparameter optimization and tuning, model assembling, and other areas. AutoML is utilized by various end-users, including industries such as banking, financial services, and insurance (BFSI), retail and e-commerce, healthcare, manufacturing, among others.

The automated machine learning (AutoML) market includes revenues earned by entities by providing data visualization, deployment of technology, monitoring and problem cracking, fraud detection, neural architecture search (NAS), and workflow optimization. The market value includes the value of related goods sold by the service provider or included within the service offering. Only goods and services traded between entities or sold to end consumers are included.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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