Automated Machine Learning (AutoML) Market Size, Share, and Trends Analysis, By Offering (Software, Services), By Deployment (On-Premises, Cloud), By Algorithm Type (Supervised, Unsupervised, Others), By Application (Data Processing, Feature Engineering, Others), By End-Use (BFSI, Retail & E-Commerce, Healthcare, Others), and By Region, Forecast 2025-2032

Automated Machine Learning (AutoML) Market Overview and Key Insights:

The global Automated Machine Learning (AutoML) market reached USD 2,132.6 Million in 2024 and is expected to register a revenue CAGR of 35.4% during the forecast period. Rising demand for simplified AI and ML model development, growing volume and complexity of data, increasing adoption of AI and data-driven decision making, growing implementation of cloud-based solutions, and rising focus on Explainable AI (XAI) and compliance are expected to drive revenue growth of the market.

Market Drivers:

Rising demand for simplified AI and ML model development is the major factor driving revenue growth of the market. Organizations across industries are rapidly adopting AI-driven solutions and need to simplify complex model-building processes that require advanced data science expertise. AutoML platforms automate tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning, enabling non-technical users and business analysts to create accurate predictive models with minimal coding.  This democratization of AI accelerates deployment, reduces operational costs, and shortens time-to-insight for enterprises.

In September 2023, for instance, Fujitsu Limited, in collaboration with the Linux Foundation, announced the open-source release of its automated machine learning and AI fairness technologies. These two initiatives provide users with access to software capable of automatically generating code for new machine learning models and tools designed to identify and mitigate hidden biases within training data. This fosters wider market adoption, promotes collaboration, and encourages commercial deployment of advanced AI solutions, and boosts demand for enterprise-grade AutoML platforms and related services.

Market Opportunity:

Rising expansion of AI applications across industries is creating significant opportunities for the market. In January 2025, U.S. President announced that the private sector would invest up to USD 500 billion to support the development of infrastructure for artificial intelligence in the country. Businesses in sectors such as healthcare, finance, manufacturing, retail, and logistics are increasingly leveraging AI to enhance decision-making, improve operational efficiency, and deliver personalized customer experiences. AutoML platforms simplify the deployment of AI by automating complex model development processes. It provides faster and more scalable integration of machine learning into business workflows.

The growing need to analyze large volumes of data and extract actionable insights without relying heavily on data science experts is fueling demand for AutoML solutions. Spending on industrial AI reached USD 43.6 billion in 2024 and is expected to rise to USD 153.9 billion by 2030. The surge in industrial AI investments reflects a broader push toward data-driven operations, predictive analytics, and intelligent automation across manufacturing, logistics, energy, and other industrial sectors. The growing need for scalable, cost-efficient AI integration is therefore fueling demand for AutoML and drives revenue growth of the market.

Recent Trends:

Democratization of machine learning through low-code and no-code platforms has emerged as a major trend of the market. The growing need to make AI and ML accessible to non-technical users has led to the development of intuitive AutoML tools that allow business analysts, domain experts, and decision-makers to build, train, and deploy machine learning models without extensive programming or data science expertise. These platforms automate critical processes such as data preparation, model selection, and hyperparameter tuning through simple drag-and-drop interfaces and guided workflows.

In March 2023, TDK Corporation introduced the first integration of an automated machine learning (ML) platform for Arm Keil MDK, developed by its group company Qeexo. The Qeexo AutoML platform supports various machine learning algorithms and is optimized for lightweight Cortex-M0 to M4 processors, offering ultra-low latency and power efficiency. It features a no-code interface that allows users to collect data and train multiple machine learning models—including both neural and non-neural network algorithms—on the same dataset, simplifying the model development process.

Restraints & Challenges:

Lack of a unified, easily accessible platform is a key factor restraining revenue growth of the market. Many AutoML tools remain fragmented across different environments, requiring complex integrations, diverse data formats, and varying levels of technical expertise. This fragmentation creates challenges for organizations seeking a seamless, end-to-end solution that can manage the entire machine learning lifecycle, from data preprocessing and model training to deployment and monitoring. The absence of standardized interfaces and interoperability between AutoML platforms limits scalability and discourages adoption, particularly among small and medium-sized enterprises with limited technical resources. It is slowing overall market expansion and restraints revenue growth of the market.

Offering Segment Insights and Analysis:

Based on the offering, the Automated Machine Learning (AutoML) market is segmented into solutions and services. Solutions segment is further sub-segmented into AutoML platforms and AutoML software modules. Services segment is further sub-segmented into professional services, managed services, and training & support.

Solutions segment contributed the largest share in 2024, due to the rising demand for comprehensive, user-friendly platforms that automate complex machine learning workflows. Organizations are increasingly investing in AutoML solutions that integrate data preprocessing, model selection, feature engineering, and deployment into a single, streamlined environment. These platforms enable faster and more efficient model development. It reduces dependence on specialized data scientists and lowers operational costs. The growing adoption of AI-driven decision-making across industries such as healthcare, finance, manufacturing, and retail is further accelerating the need for scalable and customizable AutoML solutions.

In July 2025, Analog Devices announced the general release of AutoML for Embedded, an open-source Visual Studio Code plugin designed to accelerate edge AI development. Developed in collaboration with Antmicro and integrated into ADI’s CodeFusion Studio, the tool aims to streamline the machine learning workflow for embedded developers, particularly those working with resource-constrained microcontrollers. The ease of deployment and reduced development time attract more enterprises to adopt AutoML solutions, fueling revenue growth of this segment.

Algorithm Type Insights and Analysis:

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.

Supervised learning segment contributed the largest share in 2024, due to the rising adoption of supervised learning owing to its wide applicability across diverse industries and ease of implementation. Supervised learning algorithms are extensively used for tasks such as classification, regression, and prediction, which are essential in applications like fraud detection, quality inspection, demand forecasting, and customer segmentation. AutoML platforms enhance the efficiency of supervised learning by automating data labeling, feature selection, and model optimization. It significantly reduces development time and resource requirements.

The rising adoption of Automated Quantum Machine Learning (AutoQML) is also driving revenue growth of this segment. AutoQML is developed using the sQUlearn library and seamlessly integrates with PennyLane and Qiskit, allowing it to run on quantum simulators and hardware platforms such as IBM Quantum and Amazon Braket. The framework supports supervised learning applications like time series classification, tabular regression, and image classification. It also automates algorithm selection and hyperparameter tuning through tools like Optuna and Ray Tune, simplifying quantum machine learning (QML) implementation for non-expert users.

Deployment Segment Insights and Analysis:

Based on deployment Automated Machine Learning (AutoML) market is segmented into cloud and on-premises. Cloud segment is further sub-segmented into public cloud, private cloud and hybrid cloud.

The cloud segment contributed the largest share in 2024 due to the increasing adoption of cloud-based infrastructure for scalable and flexible AI model development. Cloud deployment offers organizations the ability to handle large datasets, accelerate model training, and access advanced computational resources without significant upfront investment in hardware. AutoML platforms delivered through the cloud provide seamless integration, real-time collaboration, and automated updates. It increases operational efficiency and reduces maintenance costs.

Additionally, leading cloud service providers such as AWS, Google Cloud, and Microsoft Azure are continuously enhancing their AutoML offerings, contributing to wider adoption and substantial revenue growth in this segment. For example, Azure Machine Learning (Azure ML) is Microsoft’s cloud-based platform designed to build, train, deploy, and manage machine learning models at scale. It supports a wide range of open-source frameworks such as PyTorch, TensorFlow, and scikit-learn, allowing users to work with familiar tools while developing and deploying ML models within the Azure environment. It also offers tools that automate the training and hyperparameter tuning of machine learning models.

Application Segment Insights and Analysis:

Based on application Automated Machine Learning (AutoML) market is segmented into data processing, feature engineering, model selection, model ensembling and others.

Data processing segment contributed the largest share in 2024 due to the rising adoption of AutoML for data processing, owing to its capability for model accuracy, scalability, and operational efficiency. Enterprises today generate vast volumes of structured and unstructured data from diverse sources, driving a sharp rise in the demand for efficient data cleaning, transformation, and feature engineering. This surge has positioned the data processing segment as a crucial revenue contributor in the Automated Machine Learning (AutoML) market.

Advanced data processing capabilities in AutoML platforms streamline data preparation, minimize manual intervention, and enhance model accuracy. In July 2023, researchers at Massachusetts Institute of Technology (MIT) unveiled an open-source platform named BioAutoMATED, designed to streamline and democratize machine-learning model generation for biology-centric research labs. BioAutoMATED handles the entire workflow, covering data preprocessing, feature extraction, model selection, and training. The platform significantly reduces the time required for these tasks. It cuts down what typically takes weeks of manual effort into just a few hours.

Geographical Outlook:

Automated Machine Learning (AutoML) market is strategically segmented by geography to provide a comprehensive understanding of regional market dynamic. Discover demand analysis, emerging trends, and growth opportunities shaping market performance across different region and countries.

North America Automated Machine Learning (AutoML) Market:

Market in North America accounted for largest revenue share in 2024 due to the strong adoption of artificial intelligence and data-driven technologies across key industries such as healthcare, finance, retail, and manufacturing in the region. The region benefits from a well-established technological infrastructure, high cloud adoption rates, and the presence of major AI and AutoML vendors, including Google, Microsoft, IBM, and Amazon Web Services. Organizations are increasingly leveraging AutoML platforms to accelerate model development, reduce operational complexity, and improve decision-making efficiency.

Additionally, the growing focus on digital transformation and advancements in low-code and no-code solutions is making AutoML accessible to a wider range of users beyond traditional data scientists. In March 2025, U.S. technology giants Oracle and NVIDIA announced a major collaboration that integrates NVIDIA’s accelerated computing and inference software with Oracle’s AI infrastructure and generative AI services. Enterprises leveraging Oracle’s cloud ecosystem and NVIDIA’s accelerated computing can automate complex ML workflows with greater speed and accuracy. It promted wider adoption of AutoML across industries and fueling market expansion in the region.

Asia Pacific Automated Machine Learning (AutoML) Market:

Asia Pacific is expected to register a fast revenue growth rate during the forecast period due to the rapid digital transformation, increasing AI adoption, and expanding investments in data-driven technologies. Countries such as China, Japan, South Korea, and India are leading the shift toward automation and intelligent analytics across industries including manufacturing, finance, healthcare, and retail. The region’s growing pool of small and medium-sized enterprises is also embracing AutoML solutions to overcome the shortage of skilled data scientists and accelerate AI deployment at lower costs.

In addition, Governments and enterprises are also investing heavily in AI infrastructure, cloud computing, and large language models in the region. It creates a favourable environment for market expansion. In April 2025, China announced the launch of a substantial 60-million-yuan (approximately USD 8.2 million) state-backed fund aimed at supporting early-stage artificial intelligence projects. The growing availability of funding and infrastructure is enabling broader use of AutoML technologies across industries. It fuels strong revenue growth of the market in the region.

Europe Automated Machine Learning (AutoML) Market:

Market in Europe accounted for a significant revenue share in 2024 due to the strong focus on digital transformation, data-driven decision-making, and regulatory support for AI adoption in the region. European enterprises are increasingly deploying AutoML solutions to enhance operational efficiency, reduce dependency on scarce data science talent, and accelerate model development cycles. In February 2025, the European Union (EU) introduced InvestAI, a major initiative aimed at channelling Euro 200 billion (USD 228 billion) into artificial intelligence development. This funding is accelerating the adoption of AutoML solutions across industries by supporting research, development, and deployment of advanced machine learning applications.

Competition Analysis:

The Automated Machine Learning (AutoML) market is characterized by a fragmented structure, with numerous players competing across various segments and regions. List of major players included in the Automated Machine Learning (AutoML) market report are:

• Microsoft Corporation
• Google
• DataRobot
• H2O.ai
• Amazon Web Services
• IBM Corporation
• Dataiku
• BigML, Inc.
• dotData Inc.
• Alteryx, Inc.
• KNIME AG
• Teradata Corporation
• Oracle Corporation
• Alibaba Cloud

Strategic Developments in Automated Machine Learning (AutoML) Market:

• On 17 June 2025, Nordic Semiconductor, a global leader in ultra-low-power wireless connectivity, announced the acquisition of Neuton.AI’s intellectual property and core technology assets. Neuton.AI is known for its advanced fully automated TinyML solutions for edge devices. This acquisition marks a new phase in edge machine learning by merging Nordic’s cutting-edge nRF54 Series ultra-low-power wireless SoCs with Neuton’s innovative neural network framework.
• 14 March 2023, TDK Corporation has announced the launch of the first automated machine learning (ML) platform integration for Arm Keil MDK, developed by its group company Qeexo. The Qeexo AutoML platform supports multiple machine learning algorithms and is optimized for lightweight Cortex-M0 to M4 class processors. It offers ultra-low latency and power consumption. It enables users to efficiently utilize sensor data to develop and deploy ML solutions quickly.

Key Advantages for Stakeholders:

Navistrat Analytics’ industry report provides an in-depth quantitative analysis of various market segments, historical and current trends, market forecasts, and dynamics within the global market. The historical years covered in this report are 2022 to 2023, with 2024 serving as the base year for market size calculations. The forecast period extends from 2025 to 2032.

The report includes an executive summary and a comprehensive overview of market drivers, restraints, opportunities, and challenges (DROC), along with insights into regulatory standards. It features detailed analyses such as PORTER’s Five Forces, SWOT, and PESTLE, as well as assessments of technological trends and the competitive landscape.

PORTER’s Five Forces analysis helps stakeholders evaluate the impact of new entrants, competitive rivalry, supplier power, buyer power, and substitution threats, enabling them to assess the level of competition and the attractiveness of the global market. The competitive landscape provides stakeholders with a clear understanding of the current market positions of key players, offering valuable insights into their competitive environment.

Scope And Key Highlights of The Automated Machine Learning (AutoML) Market Report:

The Automated Machine Learning (AutoML) market report offers a detailed analysis of market size, including historical revenue (in USD Million) data for 2022-2023 and revenue forecasts for 2025-2032 across the following segments:

• Offering (Revenue, USD Million; 2022-2032)
  • Solutions
    • AutoML Platforms
    • AutoML Software Modules
  • Services
    • Professional Services
    • Managed Services
    • Training & Support

• Algorithm Type Outlook (Revenue, USD Million; 2022-2032)
  • Supervised learning
  • Unsupervised learning
  • Semi-Supervised Learning
  • Reinforcement Learning (AutoRL)
  • Deep Learning
  • Others

• Deployment Outlook (Revenue, USD Million; 2022-2032)
  • Cloud
    • Public Cloud
    • Private Cloud
    • Hybrid Cloud
  • On-Premises

• Application (Revenue, USD Million; 2022-2032)
  • Data Processing
  • Feature Engineering
  • Model Selection
  • Model Ensembling
  • Others

• End-Use (Revenue, USD Million; 2022-2032)
  • BFSI
  • Retail & E-Commerce
  • Healthcare
  • Government & Defense
  • Manufacturing
  • Media & Entertainment
  • Automotive & Transportation
  • IT & Telecommunications
  • Others

• Regional Outlook (Revenue, USD Million; 2022-2032)
  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • France
    • U.K.
    • Italy
    • Spain
    • Benelux
    • Nordic Countries
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • South Korea
    • Oceania
    • ASEAN Countries
    • Rest of APAC
  • Latin America
    • Brazil
    • Rest of LATAM
  • Middle East & Africa
    • GCC Countries
    • South Africa
    • Israel
    • Turkey
    • Rest of MEA

Frequently Asked Questions (FAQ) about the Automated Machine Learning (AutoML) Market Report