Automated Machine Learning (AutoML) Global Market Report 2025
Which drivers are expected to have the greatest impact on the over the automated machine learning (automl) market’s growth?
The growing demand for sophisticated mechanisms to detect fraud is poised to bolster the expansion of the automated machine learning (AutoML) market in the future. The process of fraud detection involves the identification and inhibition of fraudulent practices within a system or organization. AutoML technology can be applied for detecting fraud, owing to its ability to process and scrutinize an extensive amount of data, spot patterns, and identify abnormalities that could suggest fraudulent activities. For example, in February 2024, Allianz Insurance plc, a firm based in Germany that provides insurance and manages assets, reported that the claims fraud detected in 2023 amounted to $95.2 million (£77.4 million), showing an escalation from $86.96 million (£70.7 million) in 2022. Consequently, the escalating demand for advanced fraud detection solutions is fuelling the growth of the AutoML market.
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What is the future CAGR of the automated machine learning (automl) market, and how will it impact industry expansion?
The market size for automated machine learning (AutoML) has witnessed significant expansion in the recent past and is projected to climb from $1.64 billion in 2024 to $2.35 billion in 2025, boasting a compound annual growth rate (CAGR) of 43.6%. The historical growth rates stem from multiple factors including the intricacy of machine learning, a shortage of expertise in data science, the ongoing need for quick solutions, technological progress in artificial intelligence, and the affordability of computing power.
The market size for automated machine learning (AutoML) is anticipated to experience significant enhancement in the coming years, escalating to $10.93 billion in 2029 at an impressive compound annual growth rate (CAGR) of 46.8%. This anticipated growth in the projected timeline can be associated with the incorporation of AI across different industries, the proliferation of IoT and big data, the emergence of edge computing, and the expansion of hybrid cloud and on-premise solutions, in conjunction with regulatory compliance obligations. Some of the prominent trends projected for this timeframe include the automation of feature engineering, developments in federated learning, the advent of explainable AI and model interpretability, the application of AutoML to unstructured data, as well as the utilization of AutoML in autonomous systems.
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What are the most significant trends transforming the automated machine learning (automl) market today?
Advancements in technology have emerged as a critical trend in the automated machine learning (AutoML) marketplace. Major players within the AutoML marketplace are embracing these technological advancements to maintain their market status. One such instance occurred in April 2023, where AND Solutions Pte Ltd – a fintech firm from Singapore that offers AutoML platforms – introduced the NIKO AutoML platform. This groundbreaking machine-learning tool is designed to simplify and fast-track the development of predictive models. The platform offers a variety of tools and features, permitting users to swiftly and smoothly generate and establish high-quality machine learning models, regardless of their coding or data science skills level. The platform’s user interface guides users at every step, delivering optimal results much quicker than conventional methods. The NIKO AutoML platform brings multiple benefits to the table, such as rapid and precise model development, a streamlined workflow, enhanced productivity, and cost-efficiency.
Which key market segments comprise the automated machine learning (automl) market and drive its revenue growth?
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
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What regions are at the forefront of automated machine learning (automl) market expansion?
North America was the largest region in the automated machine learning (AutoML) market in 2024. 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, Western Europe, Eastern Europe, North America, South America, Middle East, Africa
Who are the leading players fueling growth in the automated machine learning (automl) market?
Major companies operating in the automated machine learning (AutoML) market include 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
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What Is Covered In The Automated Machine Learning (AutoML) Global Market Report?
• Market Size Forecast: Examine the automated machine learning (automl) market size across key regions, countries, product categories, and applications.
• Segmentation Insights: Identify and classify subsegments within the automated machine learning (automl) market for a structured understanding.
• Key Players Overview: Analyze major players in the automated machine learning (automl) market, including their market value, share, and competitive positioning.
• Growth Trends Exploration: Assess individual growth patterns and future opportunities in the automated machine learning (automl) market.
• Segment Contributions: Evaluate how different segments drive overall growth in the automated machine learning (automl) market.
• Growth Factors: Highlight key drivers and opportunities influencing the expansion of the automated machine learning (automl) market.
• Industry Challenges: Identify potential risks and obstacles affecting the automated machine learning (automl) market.
• Competitive Landscape: Review strategic developments in the automated machine learning (automl) market, including expansions, agreements, and new product launches.
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