(MENAFN- Market Press Release) November 19, 2025 3:36 am - The Automated Machine Learning (AutoML) market was valued at USD 2,132.6 Million in 2024 and is expected to register a revenue CAGR of 35.4%.

November 19, 2025 - Growing reliance on AI and data-driven decision making is a major driver of revenue growth in the Automated Machine Learning (AutoML) market. AutoML platforms simplify model development by automating tasks such as feature engineering, algorithm selection, hyperparameter tuning, and validation. It allows teams to build high-quality predictive models without deep data science expertise. This democratization of advanced analytics allows businesses across sectors, finance, healthcare, retail, manufacturing, and telecom, to deploy AI solutions rapidly and at lower cost. AutoML now become essential for turning raw data into actionable intelligence.

In September 2023, Fujitsu Limited, working with the Linux Foundation, introduced its automated machine learning and AI fairness technologies as open-source solutions. This move gives users access to tools that can automatically create code for new machine learning models and detect or reduce hidden biases in training datasets. By making these capabilities widely available, the initiative supports broader market adoption, strengthens industry collaboration, accelerates commercial use of advanced AI systems, and increases demand for enterprise-level AutoML platforms and associated services.

However, Limited transparency and model interpretability are restraining revenue growth in the Automated Machine Learning (AutoML) market because many organizations hesitate to deploy automated models they cannot fully understand or validate. AutoML tools sometimes generate complex algorithms through automated processes, making it difficult for users to trace how specific decisions or predictions are made. This lack of clarity raises concerns around accountability, regulatory compliance, and risk management, particularly in sectors such as banking, healthcare, insurance, and public services, where explainability is essential. When stakeholders cannot justify model outcomes to auditors, customers, or internal governance teams, they delay or avoid adopting AutoML solutions.

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Segments Market Overview and Growth Insights:
Based on algorithm type, the Automated Machine Learning (AutoML) market is segmented into supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, deep learning and others.

The supervised learning segment accounted for the largest share in 2024, driven by its expanding use across multiple industries and its straightforward implementation. These algorithms play a key role in classification, regression, and predictive tasks, supporting critical functions such as fraud detection, quality control, demand forecasting, and customer segmentation. Growing interest in Automated Quantum Machine Learning (AutoQML) is further strengthening this segment's revenue performance. Built on the sQUlearn library, AutoQML integrates smoothly with PennyLane and Qiskit, enabling execution on quantum simulators and quantum hardware platforms, including IBM Quantum and Amazon Braket.

Regional Market Overview And Growth Insights:
North America held the largest revenue share in 2024, supported by strong uptake of artificial intelligence and data-centric technologies across major sectors such as healthcare, finance, retail, and manufacturing. The region's advanced digital infrastructure, extensive cloud usage, and concentration of leading AI and AutoML providers-such as Google, Microsoft, IBM, and Amazon Web Services-further strengthened market growth.

In March 2025, Oracle and NVIDIA announced a significant partnership that combines NVIDIA's accelerated computing and inference capabilities with Oracle's AI infrastructure and generative AI services. This integration enables enterprises using Oracle Cloud and NVIDIA's computing stack to streamline and automate complex ML processes with higher efficiency and precision. The collaboration encouraged broader AutoML adoption across industries, driving continued growth of the regional market.

Competitive Landscape and Key Competitors:
The Automated Machine Learning (AutoML) market is characterized by a fragmented structure, with many competitors holding a significant share of the market. List of major players included in the market report are:

oMicrosoft Corporation
oGoogle
oDataRobot
oH2O
oAmazon Web Services
oIBM Corporation
oDataiku
oBigML, Inc.
odotData Inc.
oAlteryx, Inc.
oKNIME AG
oTeradata Corporation
oOracle Corporation
oAlibaba Cloud

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Major Strategic Developments By Leading Competitors:
Analog Devices: In July 2025, Analog Devices introduced the full release of AutoML for Embedded, an open-source plugin for Visual Studio Code created to speed up edge AI development. Built in partnership with Antmicro and incorporated into ADI's CodeFusion Studio, the tool is designed to simplify the machine learning pipeline for embedded developers, especially those working with microcontrollers that have limited resources.

Nordic Semiconductor: In June 2025, Nordic Semiconductor, a leading provider of ultra-low-power wireless connectivity solutions, revealed its acquisition of Neuton's intellectual property and core technology portfolio. Neuton is recognized for its highly automated TinyML capabilities designed for edge applications. This move signals a major advancement in edge machine learning, combining Nordic's state-of-the-art nRF54 Series ultra-low-power wireless SoCs with Neuton's specialized neural network framework.

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Navistrat Analytics has segmented global Automated Machine Learning (AutoML) market on the basis of Offering, Algorithm Type, deployment, application, end-use and region:

.Offering (Revenue, USD Million; 2022-2032)
oSolutions
oServices

.Algorithm Type Outlook (Revenue, USD Million; 2022-2032)
oSupervised learning
oUnsupervised learning
oSemi-Supervised Learning
oReinforcement Learning (AutoRL)
oDeep Learning
oOthers

.Deployment Outlook (Revenue, USD Million; 2022-2032)
oCloud
oOn-Premises

.Application (Revenue, USD Million; 2022-2032)
oData Processing
oFeature Engineering
oModel Selection
oModel Ensembling
oOthers

.End-Use (Revenue, USD Million; 2022-2032)
oBFSI
oRetail & E-Commerce
oHealthcare
oGovernment & Defense
oManufacturing
oMedia & Entertainment
oAutomotive & Transportation
oIT & Telecommunications
oOthers

.Regional Outlook (Revenue, USD Million; 2022-2032)
oNorth America
oEurope
oAsia Pacific
oLatin America
oMiddle East & Africa

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